import matplotlib as mpl
%matplotlib inline
from PIL import Image
import numpy as np
import pandas as pd
import os
from skimage.color import gray2rgb
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.axes_grid1 import ImageGrid
from sklearn.utils import shuffle
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import activations
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, concatenate, Dense, Dropout, Activation, Flatten, GaussianNoise, BatchNormalization, GlobalAveragePooling2D, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam, RMSprop
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.keras.models import Model
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from tensorflow.keras.models import model_from_json
from tensorflow.keras import backend as K
from tensorflow.keras.utils import to_categorical
from tf_keras_vis.gradcam import Gradcam
from tf_keras_vis.saliency import Saliency
from tf_keras_vis.utils import normalize
from sklearn.metrics import classification_report
# Define image size
mpl.rcParams['figure.figsize'] = (20,24)
After having trained and validated our CNNs, we will test them with the test data:
-338 normal MRI images from 17 control patients
-186 MRI images of periventricular nodular heterotopia (PVNH) from 6 patients
# Unzip files
!unzip ~/data/Controltest.zip -d ~/data/
!unzip ~/data/PVNHtest.zip -d ~/data/
# Remove the zipped files
!rm ~/data/Controltest.zip
!rm ~/data/PVNHtest.zip
Archive: /home/ubuntu/data/Controltest.zip inflating: /home/ubuntu/data/Controltest/1.1_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/1.10_SAGITTAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/1.11_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.12_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.13_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.14_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.15_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.16_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.17_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.18_AX MPRAGE RECON_83.jpg inflating: /home/ubuntu/data/Controltest/1.19_AX MPRAGE RECON_87.jpg inflating: /home/ubuntu/data/Controltest/1.2_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/1.20_AX MPRAGE RECON_91.jpg inflating: /home/ubuntu/data/Controltest/1.21_AX MPRAGE RECON_95.jpg inflating: /home/ubuntu/data/Controltest/1.22_AX MPRAGE RECON_100.jpg inflating: /home/ubuntu/data/Controltest/1.23_AX MPRAGE RECON_103.jpg inflating: /home/ubuntu/data/Controltest/1.24_AX MPRAGE RECON_107.jpg inflating: /home/ubuntu/data/Controltest/1.25_AX MPRAGE RECON_111.jpg inflating: /home/ubuntu/data/Controltest/1.26_AX MPRAGE RECON_115.jpg inflating: /home/ubuntu/data/Controltest/1.27_AX MPRAGE RECON_120.jpg inflating: /home/ubuntu/data/Controltest/1.28_AX MPRAGE RECON_124.jpg inflating: /home/ubuntu/data/Controltest/1.29_AX MPRAGE RECON_132.jpg inflating: /home/ubuntu/data/Controltest/1.3_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/1.30_AX T2FLAIRSPACE RECON_101.jpg inflating: /home/ubuntu/data/Controltest/1.31_AX T2FLAIRSPACE RECON_108.jpg inflating: /home/ubuntu/data/Controltest/1.32_AX T2FLAIRSPACE RECON_113.jpg inflating: /home/ubuntu/data/Controltest/1.33_AX T2FLAIRSPACE RECON_119.jpg inflating: /home/ubuntu/data/Controltest/1.34_AX T2FLAIRSPACE RECON_128.jpg inflating: /home/ubuntu/data/Controltest/1.35_COR T2FLAIRSPACE RECON_100.jpg inflating: /home/ubuntu/data/Controltest/1.36_COR T2FLAIRSPACE RECON_106.jpg inflating: /home/ubuntu/data/Controltest/1.37_COR T2FLAIRSPACE RECON_111.jpg inflating: /home/ubuntu/data/Controltest/1.38_COR T2FLAIRSPACE RECON_115.jpg inflating: /home/ubuntu/data/Controltest/1.39_COR T2FLAIRSPACE RECON_120.jpg inflating: /home/ubuntu/data/Controltest/1.4_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/1.40_COR T2FLAIRSPACE RECON_125.jpg inflating: /home/ubuntu/data/Controltest/1.41_COR T2FLAIRSPACE RECON_133.jpg inflating: /home/ubuntu/data/Controltest/1.5_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.6_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.7_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/1.8_SAGITTAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/1.9_SAGITTAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.1_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.10_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.11_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.12_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.13_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.14_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.15_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.16_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.17_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.18_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.19_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.2_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.20_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.21_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.22_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/10.23_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.24_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.25_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.26_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.27_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.28_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.29_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.3_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.30_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.31_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.32_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.33_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.34_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.35_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.36_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.4_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.5_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.6_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.7_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.8_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/10.9_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/11.1_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/11.10_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/11.11_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/11.12_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/11.13_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/11.2_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/11.3_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/11.4_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/11.5_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/11.6_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/11.7_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/11.8_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/11.9_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/12.1_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/12.10_CORONAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/12.11_CORONAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/12.12_CORONAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/12.2_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/12.3_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/12.4_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/12.5_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/12.6_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/12.7_CORONAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/12.8_CORONAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/12.9_CORONAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/13.1_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/13.2_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/13.3_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/13.4_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/13.5_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/13.6_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/14.1_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/14.10_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/14.2_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/14.3_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/14.4_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/14.5_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/14.6_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/14.7_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/14.8_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/14.9_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.1_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.10_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/15.11_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/15.12_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/15.13_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.14_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.15_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.16_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.17_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.18_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.19_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.2_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.20_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.21_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.22_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.23_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/15.3_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.4_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.5_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.6_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.7_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.8_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/15.9_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/16.1_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/16.10_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/16.11_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/16.12_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/16.13_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/16.14_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/16.15_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/16.2_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/16.3_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/16.4_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/16.5_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/16.6_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/16.7_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/16.8_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/16.9_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/17.1_COR FLAIR RECON_77.jpg inflating: /home/ubuntu/data/Controltest/17.10_COR FLAIR RECON_118.jpg inflating: /home/ubuntu/data/Controltest/17.11_COR FLAIR RECON_122.jpg inflating: /home/ubuntu/data/Controltest/17.12_COR FLAIR RECON_126.jpg inflating: /home/ubuntu/data/Controltest/17.13_COR FLAIR RECON_130.jpg inflating: /home/ubuntu/data/Controltest/17.14_COR FLAIR RECON_134.jpg inflating: /home/ubuntu/data/Controltest/17.15_COR FLAIR RECON_138.jpg inflating: /home/ubuntu/data/Controltest/17.16_COR FLAIR RECON_142.jpg inflating: /home/ubuntu/data/Controltest/17.17_AX FLAIR RECON_100.jpg inflating: /home/ubuntu/data/Controltest/17.18_AX FLAIR RECON_104.jpg inflating: /home/ubuntu/data/Controltest/17.19_AX FLAIR RECON_107.jpg inflating: /home/ubuntu/data/Controltest/17.2_COR FLAIR RECON_82.jpg inflating: /home/ubuntu/data/Controltest/17.20_AX FLAIR RECON_111.jpg inflating: /home/ubuntu/data/Controltest/17.21_AX FLAIR RECON_116.jpg inflating: /home/ubuntu/data/Controltest/17.22_AX FLAIR RECON_120.jpg inflating: /home/ubuntu/data/Controltest/17.23_AX FLAIR RECON_124.jpg inflating: /home/ubuntu/data/Controltest/17.24_AX FLAIR RECON_127.jpg inflating: /home/ubuntu/data/Controltest/17.25_AX FLAIR RECON_130.jpg inflating: /home/ubuntu/data/Controltest/17.26_AX FLAIR RECON_133.jpg inflating: /home/ubuntu/data/Controltest/17.27_AX FLAIR RECON_136.jpg inflating: /home/ubuntu/data/Controltest/17.28_AX FLAIR RECON_138.jpg inflating: /home/ubuntu/data/Controltest/17.29_AX FLAIR RECON_141.jpg inflating: /home/ubuntu/data/Controltest/17.3_COR FLAIR RECON_87.jpg inflating: /home/ubuntu/data/Controltest/17.30_AX FLAIR RECON_144.jpg inflating: /home/ubuntu/data/Controltest/17.31_AXIAL MPRAGE_100.jpg inflating: /home/ubuntu/data/Controltest/17.32_AXIAL MPRAGE_106.jpg inflating: /home/ubuntu/data/Controltest/17.33_AXIAL MPRAGE_113.jpg inflating: /home/ubuntu/data/Controltest/17.34_SAGITTAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/17.35_SAGITTAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/17.36_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/17.37_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/17.38_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/17.39_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/17.4_COR FLAIR RECON_91.jpg inflating: /home/ubuntu/data/Controltest/17.40_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/17.41_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/17.42_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/17.43_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/17.44_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/17.45_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/17.5_COR FLAIR RECON_96.jpg inflating: /home/ubuntu/data/Controltest/17.6_COR FLAIR RECON_100.jpg inflating: /home/ubuntu/data/Controltest/17.7_COR FLAIR RECON_105.jpg inflating: /home/ubuntu/data/Controltest/17.8_COR FLAIR RECON_109.jpg inflating: /home/ubuntu/data/Controltest/17.9_COR FLAIR RECON_114.jpg inflating: /home/ubuntu/data/Controltest/2.1_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.10_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/2.11_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/2.12_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/2.13_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/2.14_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/2.15_COR 4.2 FLAIR RECON_20.jpg inflating: /home/ubuntu/data/Controltest/2.16_COR 4.2 FLAIR RECON_23.jpg inflating: /home/ubuntu/data/Controltest/2.17_COR 4.2 FLAIR RECON_27.jpg inflating: /home/ubuntu/data/Controltest/2.18_COR 4.2 FLAIR RECON_31.jpg inflating: /home/ubuntu/data/Controltest/2.19_COR 4.2 FLAIR RECON_35.jpg inflating: /home/ubuntu/data/Controltest/2.2_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.3_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.4_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.5_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.6_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.7_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.8_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/2.9_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/3.1_COR FLAIR SPC RECON_120.jpg inflating: /home/ubuntu/data/Controltest/3.10_COR MPRAGE RECON_122.jpg inflating: /home/ubuntu/data/Controltest/3.11_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/3.12_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/3.13_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/3.14_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/3.15_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/3.16_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/3.17_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/3.18_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/3.2_COR FLAIR SPC RECON_132.jpg inflating: /home/ubuntu/data/Controltest/3.3_COR FLAIR SPC RECON_139.jpg inflating: /home/ubuntu/data/Controltest/3.4_COR FLAIR SPC RECON_148.jpg inflating: /home/ubuntu/data/Controltest/3.5_COR FLAIR SPC RECON_158.jpg inflating: /home/ubuntu/data/Controltest/3.6_COR MPRAGE RECON_90.jpg inflating: /home/ubuntu/data/Controltest/3.7_COR MPRAGE RECON_96.jpg inflating: /home/ubuntu/data/Controltest/3.8_COR MPRAGE RECON_105.jpg inflating: /home/ubuntu/data/Controltest/3.9_COR MPRAGE RECON_114.jpg inflating: /home/ubuntu/data/Controltest/4.1_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.10_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.11_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.12_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.13_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.2_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.3_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.4_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.7_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.8_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/4.9_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/5.1_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/5.2_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/5.3_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/5.4_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/5.5_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/5.6_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/5.7_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/5.8_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/6.1_AXIAL FLAIR SPACE RECON_110.jpg inflating: /home/ubuntu/data/Controltest/6.10_AXIAL FLAIR SPACE RECON_158.jpg inflating: /home/ubuntu/data/Controltest/6.11_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/6.12_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/6.13_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/6.14_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/6.15_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/6.16_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/6.17_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/6.18_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/6.2_AXIAL FLAIR SPACE RECON_114.jpg inflating: /home/ubuntu/data/Controltest/6.3_AXIAL FLAIR SPACE RECON_124.jpg inflating: /home/ubuntu/data/Controltest/6.4_AXIAL FLAIR SPACE RECON_132.jpg inflating: /home/ubuntu/data/Controltest/6.5_AXIAL FLAIR SPACE RECON_140.jpg inflating: /home/ubuntu/data/Controltest/6.6_AXIAL FLAIR SPACE RECON_144.jpg inflating: /home/ubuntu/data/Controltest/6.7_AXIAL FLAIR SPACE RECON_147.jpg inflating: /home/ubuntu/data/Controltest/6.8_AXIAL FLAIR SPACE RECON_151.jpg inflating: /home/ubuntu/data/Controltest/6.9_AXIAL FLAIR SPACE RECON_154.jpg inflating: /home/ubuntu/data/Controltest/7.1_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/7.10_COR FLAIR SPC RECON_160.jpg inflating: /home/ubuntu/data/Controltest/7.2_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/7.3_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/7.4_AXIAL_TSE.jpg inflating: /home/ubuntu/data/Controltest/7.5_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/7.6_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/7.7_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/7.8_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/7.9_COR FLAIR SPC RECON_123.jpg inflating: /home/ubuntu/data/Controltest/8.1_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.10_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/8.11_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/8.12_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/8.13_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/8.14_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/8.15_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/8.16_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.17_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.18_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.19_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.2_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.20_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.21_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.22_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.23_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.24_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.25_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.26_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/8.27_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/8.28_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/8.29_SAGITTAL_MPRAGE.jpg inflating: /home/ubuntu/data/Controltest/8.3_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.4_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.5_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.6_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.7_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.8_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/8.9_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.1_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.10_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/9.11_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/9.12_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/9.13_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/9.14_AXIAL_FLAIR.jpg inflating: /home/ubuntu/data/Controltest/9.15_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.16_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.17_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.18_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.19_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.2_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.20_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.21_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.22_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.23_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.24_CORONAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.3_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.4_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.5_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.6_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.7_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.8_AXIAL_T2.jpg inflating: /home/ubuntu/data/Controltest/9.9_AXIAL_FLAIR.jpg Archive: /home/ubuntu/data/PVNHtest.zip inflating: /home/ubuntu/data/PVNHtest/1.1_AX T2_35.jpg inflating: /home/ubuntu/data/PVNHtest/1.10_AX T2_47.jpg inflating: /home/ubuntu/data/PVNHtest/1.100_COR MPRAGE RECONST_115.jpg inflating: /home/ubuntu/data/PVNHtest/1.101_COR MPRAGE RECONST_116.jpg inflating: /home/ubuntu/data/PVNHtest/1.102_COR MPRAGE RECONST_117.jpg inflating: /home/ubuntu/data/PVNHtest/1.103_COR MPRAGE RECONST_118.jpg inflating: /home/ubuntu/data/PVNHtest/1.104_COR MPRAGE RECONST_119.jpg inflating: /home/ubuntu/data/PVNHtest/1.105_COR MPRAGE RECONST_120.jpg inflating: /home/ubuntu/data/PVNHtest/1.106_COR MPRAGE RECONST_121.jpg inflating: /home/ubuntu/data/PVNHtest/1.107_COR MPRAGE RECONST_122.jpg inflating: /home/ubuntu/data/PVNHtest/1.108_COR MPRAGE RECONST_123.jpg inflating: /home/ubuntu/data/PVNHtest/1.109_COR MPRAGE RECONST_124.jpg inflating: /home/ubuntu/data/PVNHtest/1.11_AXT2FLAIR_26.jpg inflating: /home/ubuntu/data/PVNHtest/1.110_COR MPRAGE RECONST_125.jpg inflating: /home/ubuntu/data/PVNHtest/1.111_COR MPRAGE RECONST_126.jpg inflating: /home/ubuntu/data/PVNHtest/1.12_AXT2FLAIR_28.jpg inflating: /home/ubuntu/data/PVNHtest/1.13_AXT2FLAIR_29.jpg inflating: /home/ubuntu/data/PVNHtest/1.14_AXT2FLAIR_30.jpg inflating: /home/ubuntu/data/PVNHtest/1.15_COR T2_26.jpg inflating: /home/ubuntu/data/PVNHtest/1.16_COR T2_28.jpg inflating: /home/ubuntu/data/PVNHtest/1.17_COR T2_30.jpg inflating: /home/ubuntu/data/PVNHtest/1.18_COR T2_32.jpg inflating: /home/ubuntu/data/PVNHtest/1.19_COR T2_34.jpg inflating: /home/ubuntu/data/PVNHtest/1.2_AX T2_39.jpg inflating: /home/ubuntu/data/PVNHtest/1.20_COR T2_36.jpg inflating: /home/ubuntu/data/PVNHtest/1.21_COR MPRAGE RECONST_72.jpg inflating: /home/ubuntu/data/PVNHtest/1.22_COR MPRAGE RECONST_75.jpg inflating: /home/ubuntu/data/PVNHtest/1.23_COR MPRAGE RECONST_78.jpg inflating: /home/ubuntu/data/PVNHtest/1.24_COR MPRAGE RECONST_81.jpg inflating: /home/ubuntu/data/PVNHtest/1.25_COR MPRAGE RECONST_84.jpg inflating: /home/ubuntu/data/PVNHtest/1.26_COR MPRAGE RECONST_87.jpg inflating: /home/ubuntu/data/PVNHtest/1.27_COR MPRAGE RECONST_90.jpg inflating: /home/ubuntu/data/PVNHtest/1.28_COR MPRAGE RECONST_93.jpg inflating: /home/ubuntu/data/PVNHtest/1.29_COR MPRAGE RECONST_96.jpg inflating: /home/ubuntu/data/PVNHtest/1.3_AX T2_40.jpg inflating: /home/ubuntu/data/PVNHtest/1.30_COR MPRAGE RECONST_99.jpg inflating: /home/ubuntu/data/PVNHtest/1.31_COR MPRAGE RECONST_102.jpg inflating: /home/ubuntu/data/PVNHtest/1.32_COR MPRAGE RECONST_105.jpg inflating: /home/ubuntu/data/PVNHtest/1.33_AXIAL MPRAGE RECONS_100.jpg inflating: /home/ubuntu/data/PVNHtest/1.34_AXIAL MPRAGE RECONS_103.jpg inflating: /home/ubuntu/data/PVNHtest/1.35_AXIAL MPRAGE RECONS_106.jpg inflating: /home/ubuntu/data/PVNHtest/1.36_AXIAL MPRAGE RECONS_109.jpg inflating: /home/ubuntu/data/PVNHtest/1.37_AXIAL MPRAGE RECONS_112.jpg inflating: /home/ubuntu/data/PVNHtest/1.38_AXIAL MPRAGE RECONS_115.jpg inflating: /home/ubuntu/data/PVNHtest/1.39_AXIAL MPRAGE RECONS_91.jpg inflating: /home/ubuntu/data/PVNHtest/1.4_AX T2_41.jpg inflating: /home/ubuntu/data/PVNHtest/1.40_AXIAL MPRAGE RECONS_92.jpg inflating: /home/ubuntu/data/PVNHtest/1.41_AXIAL MPRAGE RECONS_93.jpg inflating: /home/ubuntu/data/PVNHtest/1.42_AXIAL MPRAGE RECONS_94.jpg inflating: /home/ubuntu/data/PVNHtest/1.43_AXIAL MPRAGE RECONS_95.jpg inflating: /home/ubuntu/data/PVNHtest/1.44_AXIAL MPRAGE RECONS_96.jpg inflating: /home/ubuntu/data/PVNHtest/1.45_AXIAL MPRAGE RECONS_97.jpg inflating: /home/ubuntu/data/PVNHtest/1.46_AXIAL MPRAGE RECONS_98.jpg inflating: /home/ubuntu/data/PVNHtest/1.47_AXIAL MPRAGE RECONS_99.jpg inflating: /home/ubuntu/data/PVNHtest/1.48_AXIAL MPRAGE RECONS_101.jpg inflating: /home/ubuntu/data/PVNHtest/1.49_AXIAL MPRAGE RECONS_102.jpg inflating: /home/ubuntu/data/PVNHtest/1.5_AX T2_42.jpg inflating: /home/ubuntu/data/PVNHtest/1.50_AXIAL MPRAGE RECONS_104.jpg inflating: /home/ubuntu/data/PVNHtest/1.51_AXIAL MPRAGE RECONS_105.jpg inflating: /home/ubuntu/data/PVNHtest/1.52_AXIAL MPRAGE RECONS_107.jpg inflating: /home/ubuntu/data/PVNHtest/1.53_AXIAL MPRAGE RECONS_108.jpg inflating: /home/ubuntu/data/PVNHtest/1.54_AXIAL MPRAGE RECONS_110.jpg inflating: /home/ubuntu/data/PVNHtest/1.55_AXIAL MPRAGE RECONS_111.jpg inflating: /home/ubuntu/data/PVNHtest/1.56_AXIAL MPRAGE RECONS_113.jpg inflating: /home/ubuntu/data/PVNHtest/1.57_AXIAL MPRAGE RECONS_114.jpg inflating: /home/ubuntu/data/PVNHtest/1.58_AXIAL MPRAGE RECONS_116.jpg inflating: /home/ubuntu/data/PVNHtest/1.59_AXIAL MPRAGE RECONS_117.jpg inflating: /home/ubuntu/data/PVNHtest/1.6_AX T2_43.jpg inflating: /home/ubuntu/data/PVNHtest/1.60_COR MPRAGE RECONST_63.jpg inflating: /home/ubuntu/data/PVNHtest/1.61_COR MPRAGE RECONST_64.jpg inflating: /home/ubuntu/data/PVNHtest/1.62_COR MPRAGE RECONST_65.jpg inflating: /home/ubuntu/data/PVNHtest/1.63_COR MPRAGE RECONST_66.jpg inflating: /home/ubuntu/data/PVNHtest/1.64_COR MPRAGE RECONST_67.jpg inflating: /home/ubuntu/data/PVNHtest/1.65_COR MPRAGE RECONST_68.jpg inflating: /home/ubuntu/data/PVNHtest/1.66_COR MPRAGE RECONST_69.jpg inflating: /home/ubuntu/data/PVNHtest/1.67_COR MPRAGE RECONST_70.jpg inflating: /home/ubuntu/data/PVNHtest/1.68_COR MPRAGE RECONST_71.jpg inflating: /home/ubuntu/data/PVNHtest/1.69_COR MPRAGE RECONST_73.jpg inflating: /home/ubuntu/data/PVNHtest/1.7_AX T2_44.jpg inflating: /home/ubuntu/data/PVNHtest/1.70_COR MPRAGE RECONST_74.jpg inflating: /home/ubuntu/data/PVNHtest/1.71_COR MPRAGE RECONST_76.jpg inflating: /home/ubuntu/data/PVNHtest/1.72_COR MPRAGE RECONST_77.jpg inflating: /home/ubuntu/data/PVNHtest/1.73_COR MPRAGE RECONST_79.jpg inflating: /home/ubuntu/data/PVNHtest/1.74_COR MPRAGE RECONST_80.jpg inflating: /home/ubuntu/data/PVNHtest/1.75_COR MPRAGE RECONST_82.jpg inflating: /home/ubuntu/data/PVNHtest/1.76_COR MPRAGE RECONST_83.jpg inflating: /home/ubuntu/data/PVNHtest/1.77_COR MPRAGE RECONST_85.jpg inflating: /home/ubuntu/data/PVNHtest/1.78_COR MPRAGE RECONST_86.jpg inflating: /home/ubuntu/data/PVNHtest/1.79_COR MPRAGE RECONST_88.jpg inflating: /home/ubuntu/data/PVNHtest/1.8_AX T2_45.jpg inflating: /home/ubuntu/data/PVNHtest/1.80_COR MPRAGE RECONST_89.jpg inflating: /home/ubuntu/data/PVNHtest/1.81_COR MPRAGE RECONST_91.jpg inflating: /home/ubuntu/data/PVNHtest/1.82_COR MPRAGE RECONST_92.jpg inflating: /home/ubuntu/data/PVNHtest/1.83_COR MPRAGE RECONST_94.jpg inflating: /home/ubuntu/data/PVNHtest/1.84_COR MPRAGE RECONST_95.jpg inflating: /home/ubuntu/data/PVNHtest/1.85_COR MPRAGE RECONST_97.jpg inflating: /home/ubuntu/data/PVNHtest/1.86_COR MPRAGE RECONST_98.jpg inflating: /home/ubuntu/data/PVNHtest/1.87_COR MPRAGE RECONST_100.jpg inflating: /home/ubuntu/data/PVNHtest/1.88_COR MPRAGE RECONST_101.jpg inflating: /home/ubuntu/data/PVNHtest/1.89_COR MPRAGE RECONST_103.jpg inflating: /home/ubuntu/data/PVNHtest/1.9_AX T2_46.jpg inflating: /home/ubuntu/data/PVNHtest/1.90_COR MPRAGE RECONST_104.jpg inflating: /home/ubuntu/data/PVNHtest/1.91_COR MPRAGE RECONST_106.jpg inflating: /home/ubuntu/data/PVNHtest/1.92_COR MPRAGE RECONST_107.jpg inflating: /home/ubuntu/data/PVNHtest/1.93_COR MPRAGE RECONST_108.jpg inflating: /home/ubuntu/data/PVNHtest/1.94_COR MPRAGE RECONST_109.jpg inflating: /home/ubuntu/data/PVNHtest/1.95_COR MPRAGE RECONST_110.jpg inflating: /home/ubuntu/data/PVNHtest/1.96_COR MPRAGE RECONST_111.jpg inflating: /home/ubuntu/data/PVNHtest/1.97_COR MPRAGE RECONST_112.jpg inflating: /home/ubuntu/data/PVNHtest/1.98_COR MPRAGE RECONST_113.jpg inflating: /home/ubuntu/data/PVNHtest/1.99_COR MPRAGE RECONST_114.jpg inflating: /home/ubuntu/data/PVNHtest/2.1_AX FSE T2_39.jpg inflating: /home/ubuntu/data/PVNHtest/2.10__MPR Range__95_94.jpg inflating: /home/ubuntu/data/PVNHtest/2.11__MPR Range[1]__71_70.jpg inflating: /home/ubuntu/data/PVNHtest/2.12__MPR Range[1]__77_76.jpg inflating: /home/ubuntu/data/PVNHtest/2.13__MPR Range[1]__84_83.jpg inflating: /home/ubuntu/data/PVNHtest/2.14_AX T2 FLAIR_25.jpg inflating: /home/ubuntu/data/PVNHtest/2.15_AX T2 FLAIR_26.jpg inflating: /home/ubuntu/data/PVNHtest/2.2_AX FSE T2_40.jpg inflating: /home/ubuntu/data/PVNHtest/2.3_AX FSE T2_41.jpg inflating: /home/ubuntu/data/PVNHtest/2.4_COR T2_34.jpg inflating: /home/ubuntu/data/PVNHtest/2.5_COR T2_36.jpg inflating: /home/ubuntu/data/PVNHtest/2.6_COR T2_41.jpg inflating: /home/ubuntu/data/PVNHtest/2.7__MPR Range__92_91.jpg inflating: /home/ubuntu/data/PVNHtest/2.8__MPR Range__93_92.jpg inflating: /home/ubuntu/data/PVNHtest/2.9__MPR Range__94_93.jpg inflating: /home/ubuntu/data/PVNHtest/3.1_AX TSE T2_31.jpg inflating: /home/ubuntu/data/PVNHtest/3.10_AX MPRAGE RECON_107.jpg inflating: /home/ubuntu/data/PVNHtest/3.11_AX MPRAGE RECON_108.jpg inflating: /home/ubuntu/data/PVNHtest/3.12_COR MPRAGE RECON_74.jpg inflating: /home/ubuntu/data/PVNHtest/3.13_COR MPRAGE RECON_76.jpg inflating: /home/ubuntu/data/PVNHtest/3.14_COR MPRAGE RECON_78.jpg inflating: /home/ubuntu/data/PVNHtest/3.15_COR MPRAGE RECON_80.jpg inflating: /home/ubuntu/data/PVNHtest/3.2_AX TSE T2_32.jpg inflating: /home/ubuntu/data/PVNHtest/3.3_COR FSE T2_30.jpg inflating: /home/ubuntu/data/PVNHtest/3.4_COR FSE T2_31.jpg inflating: /home/ubuntu/data/PVNHtest/3.5_COR FSE T2_32.jpg inflating: /home/ubuntu/data/PVNHtest/3.6_COR FSE T2_33.jpg inflating: /home/ubuntu/data/PVNHtest/3.7_AX MPRAGE RECON_104.jpg inflating: /home/ubuntu/data/PVNHtest/3.8_AX MPRAGE RECON_105.jpg inflating: /home/ubuntu/data/PVNHtest/3.9_AX MPRAGE RECON_106.jpg inflating: /home/ubuntu/data/PVNHtest/4.1_AX TSE T2_26.jpg inflating: /home/ubuntu/data/PVNHtest/4.10__MPR Range[1]__70_69.jpg inflating: /home/ubuntu/data/PVNHtest/4.11__MPR Range[1]__72_71.jpg inflating: /home/ubuntu/data/PVNHtest/4.12__MPR Range[1]__74_73.jpg inflating: /home/ubuntu/data/PVNHtest/4.13__MPR Range[1]__76_75.jpg inflating: /home/ubuntu/data/PVNHtest/4.2_AX TSE T2_27.jpg inflating: /home/ubuntu/data/PVNHtest/4.3_AX TSE T2_28.jpg inflating: /home/ubuntu/data/PVNHtest/4.4__MPR Range__97_96.jpg inflating: /home/ubuntu/data/PVNHtest/4.5__MPR Range__99_98.jpg inflating: /home/ubuntu/data/PVNHtest/4.6__MPR Range__101_100.jpg inflating: /home/ubuntu/data/PVNHtest/4.7__MPR Range__103_102.jpg inflating: /home/ubuntu/data/PVNHtest/4.8__MPR Range[1]__66_65.jpg inflating: /home/ubuntu/data/PVNHtest/4.9__MPR Range[1]__68_67.jpg inflating: /home/ubuntu/data/PVNHtest/5.1_AX TSE T2_39.jpg inflating: /home/ubuntu/data/PVNHtest/5.10_COR T1 RECON_119.jpg inflating: /home/ubuntu/data/PVNHtest/5.11_COR T1 RECON_121.jpg inflating: /home/ubuntu/data/PVNHtest/5.2_AX TSE T2_40.jpg inflating: /home/ubuntu/data/PVNHtest/5.3_AX TSE T2_41.jpg inflating: /home/ubuntu/data/PVNHtest/5.4_AX TSE T2_42.jpg inflating: /home/ubuntu/data/PVNHtest/5.5_AX T2 FLAIR FS_26.jpg inflating: /home/ubuntu/data/PVNHtest/5.6_AX T2 FLAIR FS_27.jpg inflating: /home/ubuntu/data/PVNHtest/5.7_AX T1 RECON_102.jpg inflating: /home/ubuntu/data/PVNHtest/5.8_AX T1 RECON_104.jpg inflating: /home/ubuntu/data/PVNHtest/5.9_AX T1 RECON_106.jpg inflating: /home/ubuntu/data/PVNHtest/6.1_AX T2_29.jpg inflating: /home/ubuntu/data/PVNHtest/6.10_COR T2_29.jpg inflating: /home/ubuntu/data/PVNHtest/6.11_AX MPR RECONS_99.jpg inflating: /home/ubuntu/data/PVNHtest/6.12_AX MPR RECONS_101.jpg inflating: /home/ubuntu/data/PVNHtest/6.13_AX MPR RECONS_103.jpg inflating: /home/ubuntu/data/PVNHtest/6.14_COR MPR RECONS_58.jpg inflating: /home/ubuntu/data/PVNHtest/6.15_COR MPR RECONS_61.jpg inflating: /home/ubuntu/data/PVNHtest/6.16_COR MPR RECONS_64.jpg inflating: /home/ubuntu/data/PVNHtest/6.17_COR MPR RECONS_67.jpg inflating: /home/ubuntu/data/PVNHtest/6.18_COR MPR RECONS_70.jpg inflating: /home/ubuntu/data/PVNHtest/6.19_COR MPR RECONS_73.jpg inflating: /home/ubuntu/data/PVNHtest/6.2_AX T2_30.jpg inflating: /home/ubuntu/data/PVNHtest/6.20_COR MPR RECONS_76.jpg inflating: /home/ubuntu/data/PVNHtest/6.21_COR MPR RECONS_79.jpg inflating: /home/ubuntu/data/PVNHtest/6.3_AX T2_31.jpg inflating: /home/ubuntu/data/PVNHtest/6.4_AX T2 FLAIR FS_19.jpg inflating: /home/ubuntu/data/PVNHtest/6.5_AX T2 FLAIR FS_19.jpg inflating: /home/ubuntu/data/PVNHtest/6.6_COR T2_21.jpg inflating: /home/ubuntu/data/PVNHtest/6.7_COR T2_23.jpg inflating: /home/ubuntu/data/PVNHtest/6.8_COR T2_25.jpg inflating: /home/ubuntu/data/PVNHtest/6.9_COR T2_27.jpg
# Path to the folder with the original images
pathtoimagesControltest = './data/Controltest/'
pathtoimagesPVNHtest = './data/PVNHtest/'
# Create directories to save the processed images
! mkdir ~/data/processedControltest
! mkdir ~/data/processedPVNHtest
# Path to the folder with the processed images
pathtoprocessedimagesControltest = './data/processedControltest/'
pathtoprocessedimagesPVNHtest = './data/processedPVNHtest/'
# Define the image size
image_size = (512, 512)
# Read in the training images
Controltest_dir = pathtoimagesControltest
Controltest_files = os.listdir(Controltest_dir)
# For each image
for f in Controltest_files:
# Open the image
img = Image.open(Controltest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedControltest/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
# Define the image size
image_size = (512, 512)
# Read in the training images
PVNHtest_dir = pathtoimagesPVNHtest
PVNHtest_files = os.listdir(PVNHtest_dir)
# For each image
for f in PVNHtest_files:
# Open the image
img = Image.open(PVNHtest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedPVNHtest/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
# Create directories for the final images
!mkdir ~/data/FinalControltest
!mkdir ~/data/FinalPVNHtest
# Copy all processed images to the final folders
!cp ./data/processedControltest/* ./data/FinalControltest/
!cp ./data/processedPVNHtest/* ./data/FinalPVNHtest/
## Path to final images
pathtofinalControltest = './data/FinalControltest/'
pathtofinalPVNHtest = './data/FinalPVNHtest/'
## CONTROLS
# Define the image size
image_size = (512, 512)
# Read in the test images for controls
Controltest_images = []
Controltest_dir = pathtofinalControltest
Controltest_files = os.listdir(Controltest_dir)
# For each image
for f in Controltest_files:
# Open the image
img = Image.open(Controltest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
Controltest_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
Controltest_X = np.array(Controltest_images)
# Create an array of labels (0 for controls)
Controltest_y = np.array([[0]*Controltest_X.shape[0]]).T
## PVNH
# Read in the test images for PVNH
PVNHtest_images = []
PVNHtest_dir = pathtofinalPVNHtest
PVNHtest_files = os.listdir(PVNHtest_dir)
# For each image
for f in PVNHtest_files:
# Open the image
img = Image.open(PVNHtest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
PVNHtest_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
PVNHtest_X = np.array(PVNHtest_images)
# Create an array of labels (2 for PVNH)
PVNHtest_y = np.array([[1]*PVNHtest_X.shape[0]]).T
## MERGE CONTROLS AND PVNH
# Train merge files
test_X = np.concatenate([Controltest_X, PVNHtest_X])
test_y = np.vstack((Controltest_y, PVNHtest_y))
# GPU expects values to be 32-bit floats
test_X = test_X.astype(np.float32)
# Rescale the pixel values to be between 0 and 1
test_X /= 255.
# Shuffle in unison the test_X and the test_y array (123 is just a random number for reproducibility)
shuffled_test_X, shuffled_test_y = shuffle(test_X, test_y, random_state=123)
# Transform outcome to one-hot encoding
shuffled_test_y = to_categorical(shuffled_test_y)
# Make sure that the dimensions are as expected
shuffled_test_X.shape
(524, 512, 512, 3)
# Example of an image to make sure they were converted right
plt.imshow(shuffled_test_X[0])
plt.grid(b=None)
plt.xticks([])
plt.yticks([])
plt.show()
# Make sure that the dimensions are as expected
shuffled_test_y.shape
(524, 2)
# Make sure that the label is correct for the image
shuffled_test_y[0]
array([1., 0.], dtype=float32)
# load model
json_file = open('InceptionResNetV2.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("InceptionResNetV2.h5")
# Compile model
model.compile(optimizer = Adam(lr = 0.0001), loss = 'categorical_crossentropy', metrics = ['accuracy'])
# Generate predictions in the form of probabilities for the test set
testInceptionResNetV2 = model.predict(shuffled_test_X, batch_size = 32)
# Generate the confusion matrix in the test set
y_true = np.argmax(shuffled_test_y, axis=1)
y_predInceptionResNetV2 = np.argmax(testInceptionResNetV2, axis=1)
# Confusion matrix
pd.DataFrame(confusion_matrix(y_true, y_predInceptionResNetV2), index=['True: Normal', 'True: PVNH'], columns=['Prediction: Normal', 'Prediction: PVNH']).T
| True: Normal | True: PVNH | |
|---|---|---|
| Prediction: Normal | 280 | 26 |
| Prediction: PVNH | 58 | 160 |
# Calculate accuracy in the test set
accuracy_InceptionResNetV2 = accuracy_score(y_true=y_true, y_pred=y_predInceptionResNetV2)
print('The accuracy in the test set is {:.4f}.'.format(accuracy_InceptionResNetV2))
The accuracy in the test set is 0.8397.
# Calculate AUC in the test set
auc_validInceptionResNetV2 = roc_auc_score(shuffled_test_y, model.predict(shuffled_test_X))
print('The AUC in the test set is {:.4f}.'.format(auc_validInceptionResNetV2))
The AUC in the test set is 0.8998.
# Classification report
print(classification_report(y_true, y_predInceptionResNetV2, target_names=['Normal MRI', 'PVNH']))
precision recall f1-score support
Normal MRI 0.92 0.83 0.87 338
PVNH 0.73 0.86 0.79 186
accuracy 0.84 524
macro avg 0.82 0.84 0.83 524
weighted avg 0.85 0.84 0.84 524
# Visualize the structure and layers of the model
model.layers
[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x7f964aa55e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f957db54278>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa62710>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa62b38>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa62cf8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa62f28>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa19128>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa192e8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa19518>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa196d8>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f964aa19908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa19a90>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa19cc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa19e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa240f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa24320>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa24518>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f964aa24748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa248d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa24b00>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa24cc0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa24ef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa2d160>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa2d390>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa2d588>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa2d8d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa2db00>, <tensorflow.python.keras.layers.pooling.AveragePooling2D at 0x7f964aa2dbe0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa2dd68>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa2df98>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9b9208>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9b9438>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9b9668>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9b9828>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9b9b70>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9b9eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9c3240>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9c3470>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9c3550>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9c3630>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a9c3710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9c3908>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9c3b00>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9c3cf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9c3f28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa2e198>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa2e3c8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964aa2e5c0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa2e908>, <tensorflow.python.keras.layers.core.Activation at 0x7f964aa2eb38>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa2ec18>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964aa2ee48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9d40b8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9d42e8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9d44e0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9d4828>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9d4b70>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9d4da0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9d4e80>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a9d4f60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9e0080>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96531beac8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9e05c0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9e0860>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9e0a90>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9e0c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9e0e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9ee0f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9ee320>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9ee518>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9ee860>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9eea90>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9eeb70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9eeda0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9eefd0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a978240>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a978438>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a978780>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a978ac8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a978cf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a978dd8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a978eb8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a978f98>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a983278>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a983390>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9834a8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9836d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a983898>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a983ac8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a983cf8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a983f28>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a98f160>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a98f4a8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a98f6d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a98f7b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a98f9e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a98fc18>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a98fe48>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a99b080>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a99b3c8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a99b710>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a99b940>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a99ba20>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a99bb00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a99bbe0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a99be80>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a99bf98>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9a00f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9a0320>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9a04e0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9a0710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9a0940>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9a0b70>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9a0d68>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9af0f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9af320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9af400>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9af630>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9af860>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9afa90>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9afc88>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9affd0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a93a358>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a93a588>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a93a668>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a93a748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a93a828>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a93aac8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a93abe0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a93acf8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a93af28>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a93d128>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a93d358>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a93d588>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a93d7b8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a93d9b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a93dcf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a93df28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a94e048>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a94e278>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a94e4a8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a94e6d8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a94e8d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a94ec18>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a94ef60>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9581d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9582b0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a958390>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a958470>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a958710>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a958828>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a958940>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a958b70>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a958d30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a958f60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9661d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a966400>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9665f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a966940>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a966b70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a966c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a966e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a96f0f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a96f320>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a96f518>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a96f860>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a96fba8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a96fdd8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a96feb8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a96ff98>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8fc0b8>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a8fc358>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8fc470>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8fc588>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8fc7b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8fc978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8fcba8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8fcdd8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a905048>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a905240>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a905588>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9057b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a905898>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a905ac8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a905cf8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a905f28>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a913160>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9134a8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9137f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a913a20>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a913b00>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a913be0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a913cc0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a913f60>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a91c0b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a91c1d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a91c400>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a91c5c0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a91c7f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a91ca20>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a91cc50>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a91ce48>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a9261d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a926400>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a9264e0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a926710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a926940>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a926b70>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a926d68>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a9320f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a932438>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a932668>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a932748>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a932828>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a932908>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a932ba8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a932cc0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a932dd8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8ba048>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8ba208>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8ba438>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8ba668>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8ba898>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8baa90>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8badd8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8c8048>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8c8128>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8c8358>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8c8588>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8c87b8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8c89b0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8c8cf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8d4080>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8d42b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8d4390>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a8d4470>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8d4550>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a8d47f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8d4908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8d4a20>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8d4c50>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8d4e10>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8dd080>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8dd2b0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8dd4e0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8dd6d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8dda20>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8ddc50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8ddd30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8ddf60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8e91d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8e9400>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8e95f8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8e9940>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8e9c88>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8e9eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8e9f98>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a8f30b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8f3198>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a8f3438>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8f3550>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8f3668>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8f3898>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8f3a58>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8f3c88>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8f3eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a87c0f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a87c320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a87c550>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a87c780>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a87c978>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a87ccc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a87cef0>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f964a87cfd0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a889198>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a889278>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a889518>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8896d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a889908>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a889b38>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a889d30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a889f60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a88e1d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a88e400>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a88e5f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a88e940>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a88eb70>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a88ec50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a88ed30>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a88ef98>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8a10f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8a1208>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8a1438>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8a15f8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8a1828>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8a1a58>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8a1c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8a1e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8a70f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8a7320>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8a7518>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8a7860>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8a7a90>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a8a7b70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8a7c50>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a8a7eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8a7fd0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8b4128>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8b4358>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8b4518>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8b4748>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8b4978>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8b4b70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8b4da0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8b4fd0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a842240>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a842438>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a842780>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8429b0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a842a90>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a842b70>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a842dd8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a842ef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a84d048>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a84d278>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a84d438>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a84d668>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a84d898>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a84da90>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a84dcc0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a84def0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a856160>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a856358>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8566a0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8568d0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a8569b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a856a90>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a856cf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a856e10>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a856f28>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a862198>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a862358>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a862588>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8627b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8629b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a862be0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a862e10>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a86a080>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a86a278>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a86a5c0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a86a7f0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964a86a8d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a86a9b0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964a86ac18>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a86ad30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a86ae48>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8760b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a876278>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a8764a8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a8766d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964a8768d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a876b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964a876d30>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964a876f60>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467c4198>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467c44e0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467c4710>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96467c47f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467c48d0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96467c4b38>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467c4c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467c4d68>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467c4f98>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467d0198>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467d03c8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467d05f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467d07f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467d0a20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467d0c50>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467d0e80>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467db0b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467db400>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467db630>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96467db710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467db7f0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96467dba58>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467dbb70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467dbc88>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467dbeb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467e10b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467e12e8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467e1518>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467e1710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467e1940>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467e1b70>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467e1da0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467e1f98>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467f0320>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467f0550>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96467f0630>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467f0710>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96467f0978>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467f0a90>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467f0ba8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467f0dd8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467f0f98>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964677c208>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964677c438>, <tensorflow.python.keras.layers.core.Activation at 0x7f964677c630>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964677c860>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964677ca90>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964677ccc0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964677ceb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646785240>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646785470>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f9646785550>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646785630>, <tensorflow.python.keras.layers.core.Lambda at 0x7f9646785898>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467859b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646785ac8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646785cf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646785eb8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646792128>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646792358>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646792550>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646792780>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467929b0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646792be0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646792dd8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964679e160>, <tensorflow.python.keras.layers.core.Activation at 0x7f964679e390>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964679e470>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964679e550>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964679e7b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964679e8d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964679e9e8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964679ec18>, <tensorflow.python.keras.layers.core.Activation at 0x7f964679edd8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467a5048>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467a5278>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467a5470>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467a56a0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467a58d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467a5b00>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467a5cf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467ad080>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467ad2b0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96467ad390>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467ad470>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96467ad6d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467ad7f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467ad908>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467adb38>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467adcf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467adf28>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964673b198>, <tensorflow.python.keras.layers.core.Activation at 0x7f964673b390>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964673b5c0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964673b7f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964673ba20>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964673bc18>, <tensorflow.python.keras.layers.core.Activation at 0x7f964673bf60>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467481d0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96467482b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646748390>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96467485f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646748710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646748828>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646748a58>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646748c18>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646748e48>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467510b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467512b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467514e0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646751710>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646751940>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646751b38>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646751e80>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467610f0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96467611d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467612b0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f9646761518>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646761630>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646761748>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646761978>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646761b38>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646761d68>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646761f98>, <tensorflow.python.keras.layers.core.Activation at 0x7f964676a1d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964676a400>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964676a630>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964676a860>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964676aa58>, <tensorflow.python.keras.layers.core.Activation at 0x7f964676ada0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964676afd0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96467720f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467721d0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f9646772438>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646772550>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646772668>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646772898>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646772a58>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646772c88>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646772eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467010f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646701320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646701550>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646701780>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646701978>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646701cc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646701ef0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f9646701fd0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964670a0f0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964670a358>, <tensorflow.python.keras.layers.core.Activation at 0x7f964670a470>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964670a588>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964670a7b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964670a978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964670aba8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964670add8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964670afd0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646714240>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646714470>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467146a0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646714898>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646714be0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646714e10>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f9646714ef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646714fd0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964671c278>, <tensorflow.python.keras.layers.core.Activation at 0x7f964671c390>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964671c4a8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964671c6d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964671c898>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964671cac8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964671ccf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964671cef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964672d160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964672d390>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964672d5c0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964672d7b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964672db00>, <tensorflow.python.keras.layers.core.Activation at 0x7f964672dd30>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964672de10>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964672def0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f9646734198>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467342b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467343c8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96467345f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96467347b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96467349e8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646734c18>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646734e10>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466bd080>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466bd2b0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466bd4e0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466bd6d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466bda20>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466bdc50>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96466bdd30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466bde10>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96466cd0b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466cd1d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466cd2e8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466cd518>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466cd6d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466cd908>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466cdb38>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466cdd30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466cdf60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466d71d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466d7400>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466d75f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466d7940>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466d7b70>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96466d7c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466d7d30>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96466d7f98>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466e30f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466e3208>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466e3438>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466e35f8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466e3828>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466e3a58>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466e3c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466e3e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466ef0f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466ef320>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466ef518>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466ef860>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466efa90>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96466efb70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466efc50>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96466efeb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466effd0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466f8128>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466f8358>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466f8518>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466f8748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466f8978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466f8ba8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466f8dd8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466f8fd0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646684358>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466846a0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466848d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466849b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646684a90>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646684cc0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646684ef0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964668e160>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964668e358>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964668e6a0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964668e9e8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964668ec18>, <tensorflow.python.keras.layers.core.Activation at 0x7f964668ecf8>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f964668edd8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964668ef60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646696198>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646696390>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646696550>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646696780>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466969b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646696ba8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646696dd8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466a4048>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466a4278>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466a4470>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466a47b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466a49e8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96466a4ac8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466a4ba8>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96466a4e10>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466a4f28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466ae080>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466ae2b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466ae470>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466ae6a0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466ae8d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466aeac8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466aecf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466aef28>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964663a198>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964663a390>, <tensorflow.python.keras.layers.core.Activation at 0x7f964663a6d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964663a908>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964663a9e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964663aac8>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964663ad30>, <tensorflow.python.keras.layers.core.Activation at 0x7f964663ae48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964663af60>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466401d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646640390>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466405c0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466407f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466409e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646640c18>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646640e48>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964664f0b8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964664f2b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964664f5f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f964664f828>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964664f908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964664f9e8>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964664fc50>, <tensorflow.python.keras.layers.core.Activation at 0x7f964664fd68>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964664fe80>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964665b0f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964665b2b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964665b4e0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964665b710>, <tensorflow.python.keras.layers.core.Activation at 0x7f964665b908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964665bb38>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964665bd68>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964665bf98>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466651d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646665518>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646665748>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f9646665828>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646665908>, <tensorflow.python.keras.layers.core.Lambda at 0x7f9646665b70>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646665c88>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646665da0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646665fd0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466701d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646670400>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646670630>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646670828>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646670a58>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646670c88>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646670eb8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96466780f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646678438>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646678668>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f9646678748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646678828>, <tensorflow.python.keras.layers.core.Lambda at 0x7f9646678a90>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646678ba8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646678cc0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646678ef0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96466080f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646608320>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646608550>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646608748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646608978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646608ba8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646608dd8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646608fd0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646611358>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646611588>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f9646611668>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646611748>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96466119b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646611ac8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646611be0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646611e10>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646611fd0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964661c240>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964661c470>, <tensorflow.python.keras.layers.core.Activation at 0x7f964661c668>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964661c898>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964661cac8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964661ccf8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964661cef0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964662a278>, <tensorflow.python.keras.layers.core.Activation at 0x7f964662a4a8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f964662a588>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964662a668>, <tensorflow.python.keras.layers.core.Lambda at 0x7f964662a8d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f964662a9e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f964662ab00>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f964662ad30>, <tensorflow.python.keras.layers.core.Activation at 0x7f964662aef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f9646630160>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646630390>, <tensorflow.python.keras.layers.core.Activation at 0x7f9646630588>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466307b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96466309e8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646630c18>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f9646630e10>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465c1198>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465c13c8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96465c14a8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465c1588>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96465c17f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465c1908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465c1a20>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465c1c50>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465c1e10>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465ca080>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465ca2b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465ca4a8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465ca6d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465ca908>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465cab38>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465cad30>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465d50b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465d52e8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96465d53c8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465d54a8>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96465d5710>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465d5828>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465d5940>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465d5b70>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465d5d30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465d5f60>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465db1d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465db3c8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465db5f8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465db828>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465dba58>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465dbc50>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465dbf98>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465ea208>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f96465ea2e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465ea3c8>, <tensorflow.python.keras.layers.core.Lambda at 0x7f96465ea630>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f96465ea748>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7f96465ea9b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f96465eab70>, <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D at 0x7f96465eada0>, <tensorflow.python.keras.layers.core.Dense at 0x7f96465eaeb8>, <tensorflow.python.keras.layers.core.Dropout at 0x7f96465f60f0>, <tensorflow.python.keras.layers.core.Dense at 0x7f96465f61d0>, <tensorflow.python.keras.layers.core.Dropout at 0x7f96465f6400>, <tensorflow.python.keras.layers.core.Dense at 0x7f96465f64e0>, <tensorflow.python.keras.layers.core.Dense at 0x7f96465f6710>]
# Visualize the structure and layers of the model
print(model.summary())
Model: "model_123"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) [(None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv2d_129 (Conv2D) (None, 255, 255, 32) 864 input_4[0][0]
__________________________________________________________________________________________________
batch_normalization_98 (BatchNo (None, 255, 255, 32) 96 conv2d_129[0][0]
__________________________________________________________________________________________________
activation_98 (Activation) (None, 255, 255, 32) 0 batch_normalization_98[0][0]
__________________________________________________________________________________________________
conv2d_130 (Conv2D) (None, 253, 253, 32) 9216 activation_98[0][0]
__________________________________________________________________________________________________
batch_normalization_99 (BatchNo (None, 253, 253, 32) 96 conv2d_130[0][0]
__________________________________________________________________________________________________
activation_99 (Activation) (None, 253, 253, 32) 0 batch_normalization_99[0][0]
__________________________________________________________________________________________________
conv2d_131 (Conv2D) (None, 253, 253, 64) 18432 activation_99[0][0]
__________________________________________________________________________________________________
batch_normalization_100 (BatchN (None, 253, 253, 64) 192 conv2d_131[0][0]
__________________________________________________________________________________________________
activation_100 (Activation) (None, 253, 253, 64) 0 batch_normalization_100[0][0]
__________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, 126, 126, 64) 0 activation_100[0][0]
__________________________________________________________________________________________________
conv2d_132 (Conv2D) (None, 126, 126, 80) 5120 max_pooling2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_101 (BatchN (None, 126, 126, 80) 240 conv2d_132[0][0]
__________________________________________________________________________________________________
activation_101 (Activation) (None, 126, 126, 80) 0 batch_normalization_101[0][0]
__________________________________________________________________________________________________
conv2d_133 (Conv2D) (None, 124, 124, 192 138240 activation_101[0][0]
__________________________________________________________________________________________________
batch_normalization_102 (BatchN (None, 124, 124, 192 576 conv2d_133[0][0]
__________________________________________________________________________________________________
activation_102 (Activation) (None, 124, 124, 192 0 batch_normalization_102[0][0]
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 61, 61, 192) 0 activation_102[0][0]
__________________________________________________________________________________________________
conv2d_137 (Conv2D) (None, 61, 61, 64) 12288 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
batch_normalization_106 (BatchN (None, 61, 61, 64) 192 conv2d_137[0][0]
__________________________________________________________________________________________________
activation_106 (Activation) (None, 61, 61, 64) 0 batch_normalization_106[0][0]
__________________________________________________________________________________________________
conv2d_135 (Conv2D) (None, 61, 61, 48) 9216 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_138 (Conv2D) (None, 61, 61, 96) 55296 activation_106[0][0]
__________________________________________________________________________________________________
batch_normalization_104 (BatchN (None, 61, 61, 48) 144 conv2d_135[0][0]
__________________________________________________________________________________________________
batch_normalization_107 (BatchN (None, 61, 61, 96) 288 conv2d_138[0][0]
__________________________________________________________________________________________________
activation_104 (Activation) (None, 61, 61, 48) 0 batch_normalization_104[0][0]
__________________________________________________________________________________________________
activation_107 (Activation) (None, 61, 61, 96) 0 batch_normalization_107[0][0]
__________________________________________________________________________________________________
average_pooling2d_9 (AveragePoo (None, 61, 61, 192) 0 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_134 (Conv2D) (None, 61, 61, 96) 18432 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_136 (Conv2D) (None, 61, 61, 64) 76800 activation_104[0][0]
__________________________________________________________________________________________________
conv2d_139 (Conv2D) (None, 61, 61, 96) 82944 activation_107[0][0]
__________________________________________________________________________________________________
conv2d_140 (Conv2D) (None, 61, 61, 64) 12288 average_pooling2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_103 (BatchN (None, 61, 61, 96) 288 conv2d_134[0][0]
__________________________________________________________________________________________________
batch_normalization_105 (BatchN (None, 61, 61, 64) 192 conv2d_136[0][0]
__________________________________________________________________________________________________
batch_normalization_108 (BatchN (None, 61, 61, 96) 288 conv2d_139[0][0]
__________________________________________________________________________________________________
batch_normalization_109 (BatchN (None, 61, 61, 64) 192 conv2d_140[0][0]
__________________________________________________________________________________________________
activation_103 (Activation) (None, 61, 61, 96) 0 batch_normalization_103[0][0]
__________________________________________________________________________________________________
activation_105 (Activation) (None, 61, 61, 64) 0 batch_normalization_105[0][0]
__________________________________________________________________________________________________
activation_108 (Activation) (None, 61, 61, 96) 0 batch_normalization_108[0][0]
__________________________________________________________________________________________________
activation_109 (Activation) (None, 61, 61, 64) 0 batch_normalization_109[0][0]
__________________________________________________________________________________________________
mixed_5b (Concatenate) (None, 61, 61, 320) 0 activation_103[0][0]
activation_105[0][0]
activation_108[0][0]
activation_109[0][0]
__________________________________________________________________________________________________
conv2d_144 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
batch_normalization_113 (BatchN (None, 61, 61, 32) 96 conv2d_144[0][0]
__________________________________________________________________________________________________
activation_113 (Activation) (None, 61, 61, 32) 0 batch_normalization_113[0][0]
__________________________________________________________________________________________________
conv2d_142 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
conv2d_145 (Conv2D) (None, 61, 61, 48) 13824 activation_113[0][0]
__________________________________________________________________________________________________
batch_normalization_111 (BatchN (None, 61, 61, 32) 96 conv2d_142[0][0]
__________________________________________________________________________________________________
batch_normalization_114 (BatchN (None, 61, 61, 48) 144 conv2d_145[0][0]
__________________________________________________________________________________________________
activation_111 (Activation) (None, 61, 61, 32) 0 batch_normalization_111[0][0]
__________________________________________________________________________________________________
activation_114 (Activation) (None, 61, 61, 48) 0 batch_normalization_114[0][0]
__________________________________________________________________________________________________
conv2d_141 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
conv2d_143 (Conv2D) (None, 61, 61, 32) 9216 activation_111[0][0]
__________________________________________________________________________________________________
conv2d_146 (Conv2D) (None, 61, 61, 64) 27648 activation_114[0][0]
__________________________________________________________________________________________________
batch_normalization_110 (BatchN (None, 61, 61, 32) 96 conv2d_141[0][0]
__________________________________________________________________________________________________
batch_normalization_112 (BatchN (None, 61, 61, 32) 96 conv2d_143[0][0]
__________________________________________________________________________________________________
batch_normalization_115 (BatchN (None, 61, 61, 64) 192 conv2d_146[0][0]
__________________________________________________________________________________________________
activation_110 (Activation) (None, 61, 61, 32) 0 batch_normalization_110[0][0]
__________________________________________________________________________________________________
activation_112 (Activation) (None, 61, 61, 32) 0 batch_normalization_112[0][0]
__________________________________________________________________________________________________
activation_115 (Activation) (None, 61, 61, 64) 0 batch_normalization_115[0][0]
__________________________________________________________________________________________________
block35_1_mixed (Concatenate) (None, 61, 61, 128) 0 activation_110[0][0]
activation_112[0][0]
activation_115[0][0]
__________________________________________________________________________________________________
block35_1_conv (Conv2D) (None, 61, 61, 320) 41280 block35_1_mixed[0][0]
__________________________________________________________________________________________________
block35_1 (Lambda) (None, 61, 61, 320) 0 mixed_5b[0][0]
block35_1_conv[0][0]
__________________________________________________________________________________________________
block35_1_ac (Activation) (None, 61, 61, 320) 0 block35_1[0][0]
__________________________________________________________________________________________________
conv2d_150 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_119 (BatchN (None, 61, 61, 32) 96 conv2d_150[0][0]
__________________________________________________________________________________________________
activation_119 (Activation) (None, 61, 61, 32) 0 batch_normalization_119[0][0]
__________________________________________________________________________________________________
conv2d_148 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_151 (Conv2D) (None, 61, 61, 48) 13824 activation_119[0][0]
__________________________________________________________________________________________________
batch_normalization_117 (BatchN (None, 61, 61, 32) 96 conv2d_148[0][0]
__________________________________________________________________________________________________
batch_normalization_120 (BatchN (None, 61, 61, 48) 144 conv2d_151[0][0]
__________________________________________________________________________________________________
activation_117 (Activation) (None, 61, 61, 32) 0 batch_normalization_117[0][0]
__________________________________________________________________________________________________
activation_120 (Activation) (None, 61, 61, 48) 0 batch_normalization_120[0][0]
__________________________________________________________________________________________________
conv2d_147 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_149 (Conv2D) (None, 61, 61, 32) 9216 activation_117[0][0]
__________________________________________________________________________________________________
conv2d_152 (Conv2D) (None, 61, 61, 64) 27648 activation_120[0][0]
__________________________________________________________________________________________________
batch_normalization_116 (BatchN (None, 61, 61, 32) 96 conv2d_147[0][0]
__________________________________________________________________________________________________
batch_normalization_118 (BatchN (None, 61, 61, 32) 96 conv2d_149[0][0]
__________________________________________________________________________________________________
batch_normalization_121 (BatchN (None, 61, 61, 64) 192 conv2d_152[0][0]
__________________________________________________________________________________________________
activation_116 (Activation) (None, 61, 61, 32) 0 batch_normalization_116[0][0]
__________________________________________________________________________________________________
activation_118 (Activation) (None, 61, 61, 32) 0 batch_normalization_118[0][0]
__________________________________________________________________________________________________
activation_121 (Activation) (None, 61, 61, 64) 0 batch_normalization_121[0][0]
__________________________________________________________________________________________________
block35_2_mixed (Concatenate) (None, 61, 61, 128) 0 activation_116[0][0]
activation_118[0][0]
activation_121[0][0]
__________________________________________________________________________________________________
block35_2_conv (Conv2D) (None, 61, 61, 320) 41280 block35_2_mixed[0][0]
__________________________________________________________________________________________________
block35_2 (Lambda) (None, 61, 61, 320) 0 block35_1_ac[0][0]
block35_2_conv[0][0]
__________________________________________________________________________________________________
block35_2_ac (Activation) (None, 61, 61, 320) 0 block35_2[0][0]
__________________________________________________________________________________________________
conv2d_156 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_125 (BatchN (None, 61, 61, 32) 96 conv2d_156[0][0]
__________________________________________________________________________________________________
activation_125 (Activation) (None, 61, 61, 32) 0 batch_normalization_125[0][0]
__________________________________________________________________________________________________
conv2d_154 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_157 (Conv2D) (None, 61, 61, 48) 13824 activation_125[0][0]
__________________________________________________________________________________________________
batch_normalization_123 (BatchN (None, 61, 61, 32) 96 conv2d_154[0][0]
__________________________________________________________________________________________________
batch_normalization_126 (BatchN (None, 61, 61, 48) 144 conv2d_157[0][0]
__________________________________________________________________________________________________
activation_123 (Activation) (None, 61, 61, 32) 0 batch_normalization_123[0][0]
__________________________________________________________________________________________________
activation_126 (Activation) (None, 61, 61, 48) 0 batch_normalization_126[0][0]
__________________________________________________________________________________________________
conv2d_153 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_155 (Conv2D) (None, 61, 61, 32) 9216 activation_123[0][0]
__________________________________________________________________________________________________
conv2d_158 (Conv2D) (None, 61, 61, 64) 27648 activation_126[0][0]
__________________________________________________________________________________________________
batch_normalization_122 (BatchN (None, 61, 61, 32) 96 conv2d_153[0][0]
__________________________________________________________________________________________________
batch_normalization_124 (BatchN (None, 61, 61, 32) 96 conv2d_155[0][0]
__________________________________________________________________________________________________
batch_normalization_127 (BatchN (None, 61, 61, 64) 192 conv2d_158[0][0]
__________________________________________________________________________________________________
activation_122 (Activation) (None, 61, 61, 32) 0 batch_normalization_122[0][0]
__________________________________________________________________________________________________
activation_124 (Activation) (None, 61, 61, 32) 0 batch_normalization_124[0][0]
__________________________________________________________________________________________________
activation_127 (Activation) (None, 61, 61, 64) 0 batch_normalization_127[0][0]
__________________________________________________________________________________________________
block35_3_mixed (Concatenate) (None, 61, 61, 128) 0 activation_122[0][0]
activation_124[0][0]
activation_127[0][0]
__________________________________________________________________________________________________
block35_3_conv (Conv2D) (None, 61, 61, 320) 41280 block35_3_mixed[0][0]
__________________________________________________________________________________________________
block35_3 (Lambda) (None, 61, 61, 320) 0 block35_2_ac[0][0]
block35_3_conv[0][0]
__________________________________________________________________________________________________
block35_3_ac (Activation) (None, 61, 61, 320) 0 block35_3[0][0]
__________________________________________________________________________________________________
conv2d_162 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_131 (BatchN (None, 61, 61, 32) 96 conv2d_162[0][0]
__________________________________________________________________________________________________
activation_131 (Activation) (None, 61, 61, 32) 0 batch_normalization_131[0][0]
__________________________________________________________________________________________________
conv2d_160 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_163 (Conv2D) (None, 61, 61, 48) 13824 activation_131[0][0]
__________________________________________________________________________________________________
batch_normalization_129 (BatchN (None, 61, 61, 32) 96 conv2d_160[0][0]
__________________________________________________________________________________________________
batch_normalization_132 (BatchN (None, 61, 61, 48) 144 conv2d_163[0][0]
__________________________________________________________________________________________________
activation_129 (Activation) (None, 61, 61, 32) 0 batch_normalization_129[0][0]
__________________________________________________________________________________________________
activation_132 (Activation) (None, 61, 61, 48) 0 batch_normalization_132[0][0]
__________________________________________________________________________________________________
conv2d_159 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_161 (Conv2D) (None, 61, 61, 32) 9216 activation_129[0][0]
__________________________________________________________________________________________________
conv2d_164 (Conv2D) (None, 61, 61, 64) 27648 activation_132[0][0]
__________________________________________________________________________________________________
batch_normalization_128 (BatchN (None, 61, 61, 32) 96 conv2d_159[0][0]
__________________________________________________________________________________________________
batch_normalization_130 (BatchN (None, 61, 61, 32) 96 conv2d_161[0][0]
__________________________________________________________________________________________________
batch_normalization_133 (BatchN (None, 61, 61, 64) 192 conv2d_164[0][0]
__________________________________________________________________________________________________
activation_128 (Activation) (None, 61, 61, 32) 0 batch_normalization_128[0][0]
__________________________________________________________________________________________________
activation_130 (Activation) (None, 61, 61, 32) 0 batch_normalization_130[0][0]
__________________________________________________________________________________________________
activation_133 (Activation) (None, 61, 61, 64) 0 batch_normalization_133[0][0]
__________________________________________________________________________________________________
block35_4_mixed (Concatenate) (None, 61, 61, 128) 0 activation_128[0][0]
activation_130[0][0]
activation_133[0][0]
__________________________________________________________________________________________________
block35_4_conv (Conv2D) (None, 61, 61, 320) 41280 block35_4_mixed[0][0]
__________________________________________________________________________________________________
block35_4 (Lambda) (None, 61, 61, 320) 0 block35_3_ac[0][0]
block35_4_conv[0][0]
__________________________________________________________________________________________________
block35_4_ac (Activation) (None, 61, 61, 320) 0 block35_4[0][0]
__________________________________________________________________________________________________
conv2d_168 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_137 (BatchN (None, 61, 61, 32) 96 conv2d_168[0][0]
__________________________________________________________________________________________________
activation_137 (Activation) (None, 61, 61, 32) 0 batch_normalization_137[0][0]
__________________________________________________________________________________________________
conv2d_166 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_169 (Conv2D) (None, 61, 61, 48) 13824 activation_137[0][0]
__________________________________________________________________________________________________
batch_normalization_135 (BatchN (None, 61, 61, 32) 96 conv2d_166[0][0]
__________________________________________________________________________________________________
batch_normalization_138 (BatchN (None, 61, 61, 48) 144 conv2d_169[0][0]
__________________________________________________________________________________________________
activation_135 (Activation) (None, 61, 61, 32) 0 batch_normalization_135[0][0]
__________________________________________________________________________________________________
activation_138 (Activation) (None, 61, 61, 48) 0 batch_normalization_138[0][0]
__________________________________________________________________________________________________
conv2d_165 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_167 (Conv2D) (None, 61, 61, 32) 9216 activation_135[0][0]
__________________________________________________________________________________________________
conv2d_170 (Conv2D) (None, 61, 61, 64) 27648 activation_138[0][0]
__________________________________________________________________________________________________
batch_normalization_134 (BatchN (None, 61, 61, 32) 96 conv2d_165[0][0]
__________________________________________________________________________________________________
batch_normalization_136 (BatchN (None, 61, 61, 32) 96 conv2d_167[0][0]
__________________________________________________________________________________________________
batch_normalization_139 (BatchN (None, 61, 61, 64) 192 conv2d_170[0][0]
__________________________________________________________________________________________________
activation_134 (Activation) (None, 61, 61, 32) 0 batch_normalization_134[0][0]
__________________________________________________________________________________________________
activation_136 (Activation) (None, 61, 61, 32) 0 batch_normalization_136[0][0]
__________________________________________________________________________________________________
activation_139 (Activation) (None, 61, 61, 64) 0 batch_normalization_139[0][0]
__________________________________________________________________________________________________
block35_5_mixed (Concatenate) (None, 61, 61, 128) 0 activation_134[0][0]
activation_136[0][0]
activation_139[0][0]
__________________________________________________________________________________________________
block35_5_conv (Conv2D) (None, 61, 61, 320) 41280 block35_5_mixed[0][0]
__________________________________________________________________________________________________
block35_5 (Lambda) (None, 61, 61, 320) 0 block35_4_ac[0][0]
block35_5_conv[0][0]
__________________________________________________________________________________________________
block35_5_ac (Activation) (None, 61, 61, 320) 0 block35_5[0][0]
__________________________________________________________________________________________________
conv2d_174 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_143 (BatchN (None, 61, 61, 32) 96 conv2d_174[0][0]
__________________________________________________________________________________________________
activation_143 (Activation) (None, 61, 61, 32) 0 batch_normalization_143[0][0]
__________________________________________________________________________________________________
conv2d_172 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_175 (Conv2D) (None, 61, 61, 48) 13824 activation_143[0][0]
__________________________________________________________________________________________________
batch_normalization_141 (BatchN (None, 61, 61, 32) 96 conv2d_172[0][0]
__________________________________________________________________________________________________
batch_normalization_144 (BatchN (None, 61, 61, 48) 144 conv2d_175[0][0]
__________________________________________________________________________________________________
activation_141 (Activation) (None, 61, 61, 32) 0 batch_normalization_141[0][0]
__________________________________________________________________________________________________
activation_144 (Activation) (None, 61, 61, 48) 0 batch_normalization_144[0][0]
__________________________________________________________________________________________________
conv2d_171 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_173 (Conv2D) (None, 61, 61, 32) 9216 activation_141[0][0]
__________________________________________________________________________________________________
conv2d_176 (Conv2D) (None, 61, 61, 64) 27648 activation_144[0][0]
__________________________________________________________________________________________________
batch_normalization_140 (BatchN (None, 61, 61, 32) 96 conv2d_171[0][0]
__________________________________________________________________________________________________
batch_normalization_142 (BatchN (None, 61, 61, 32) 96 conv2d_173[0][0]
__________________________________________________________________________________________________
batch_normalization_145 (BatchN (None, 61, 61, 64) 192 conv2d_176[0][0]
__________________________________________________________________________________________________
activation_140 (Activation) (None, 61, 61, 32) 0 batch_normalization_140[0][0]
__________________________________________________________________________________________________
activation_142 (Activation) (None, 61, 61, 32) 0 batch_normalization_142[0][0]
__________________________________________________________________________________________________
activation_145 (Activation) (None, 61, 61, 64) 0 batch_normalization_145[0][0]
__________________________________________________________________________________________________
block35_6_mixed (Concatenate) (None, 61, 61, 128) 0 activation_140[0][0]
activation_142[0][0]
activation_145[0][0]
__________________________________________________________________________________________________
block35_6_conv (Conv2D) (None, 61, 61, 320) 41280 block35_6_mixed[0][0]
__________________________________________________________________________________________________
block35_6 (Lambda) (None, 61, 61, 320) 0 block35_5_ac[0][0]
block35_6_conv[0][0]
__________________________________________________________________________________________________
block35_6_ac (Activation) (None, 61, 61, 320) 0 block35_6[0][0]
__________________________________________________________________________________________________
conv2d_180 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_149 (BatchN (None, 61, 61, 32) 96 conv2d_180[0][0]
__________________________________________________________________________________________________
activation_149 (Activation) (None, 61, 61, 32) 0 batch_normalization_149[0][0]
__________________________________________________________________________________________________
conv2d_178 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_181 (Conv2D) (None, 61, 61, 48) 13824 activation_149[0][0]
__________________________________________________________________________________________________
batch_normalization_147 (BatchN (None, 61, 61, 32) 96 conv2d_178[0][0]
__________________________________________________________________________________________________
batch_normalization_150 (BatchN (None, 61, 61, 48) 144 conv2d_181[0][0]
__________________________________________________________________________________________________
activation_147 (Activation) (None, 61, 61, 32) 0 batch_normalization_147[0][0]
__________________________________________________________________________________________________
activation_150 (Activation) (None, 61, 61, 48) 0 batch_normalization_150[0][0]
__________________________________________________________________________________________________
conv2d_177 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_179 (Conv2D) (None, 61, 61, 32) 9216 activation_147[0][0]
__________________________________________________________________________________________________
conv2d_182 (Conv2D) (None, 61, 61, 64) 27648 activation_150[0][0]
__________________________________________________________________________________________________
batch_normalization_146 (BatchN (None, 61, 61, 32) 96 conv2d_177[0][0]
__________________________________________________________________________________________________
batch_normalization_148 (BatchN (None, 61, 61, 32) 96 conv2d_179[0][0]
__________________________________________________________________________________________________
batch_normalization_151 (BatchN (None, 61, 61, 64) 192 conv2d_182[0][0]
__________________________________________________________________________________________________
activation_146 (Activation) (None, 61, 61, 32) 0 batch_normalization_146[0][0]
__________________________________________________________________________________________________
activation_148 (Activation) (None, 61, 61, 32) 0 batch_normalization_148[0][0]
__________________________________________________________________________________________________
activation_151 (Activation) (None, 61, 61, 64) 0 batch_normalization_151[0][0]
__________________________________________________________________________________________________
block35_7_mixed (Concatenate) (None, 61, 61, 128) 0 activation_146[0][0]
activation_148[0][0]
activation_151[0][0]
__________________________________________________________________________________________________
block35_7_conv (Conv2D) (None, 61, 61, 320) 41280 block35_7_mixed[0][0]
__________________________________________________________________________________________________
block35_7 (Lambda) (None, 61, 61, 320) 0 block35_6_ac[0][0]
block35_7_conv[0][0]
__________________________________________________________________________________________________
block35_7_ac (Activation) (None, 61, 61, 320) 0 block35_7[0][0]
__________________________________________________________________________________________________
conv2d_186 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_155 (BatchN (None, 61, 61, 32) 96 conv2d_186[0][0]
__________________________________________________________________________________________________
activation_155 (Activation) (None, 61, 61, 32) 0 batch_normalization_155[0][0]
__________________________________________________________________________________________________
conv2d_184 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_187 (Conv2D) (None, 61, 61, 48) 13824 activation_155[0][0]
__________________________________________________________________________________________________
batch_normalization_153 (BatchN (None, 61, 61, 32) 96 conv2d_184[0][0]
__________________________________________________________________________________________________
batch_normalization_156 (BatchN (None, 61, 61, 48) 144 conv2d_187[0][0]
__________________________________________________________________________________________________
activation_153 (Activation) (None, 61, 61, 32) 0 batch_normalization_153[0][0]
__________________________________________________________________________________________________
activation_156 (Activation) (None, 61, 61, 48) 0 batch_normalization_156[0][0]
__________________________________________________________________________________________________
conv2d_183 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_185 (Conv2D) (None, 61, 61, 32) 9216 activation_153[0][0]
__________________________________________________________________________________________________
conv2d_188 (Conv2D) (None, 61, 61, 64) 27648 activation_156[0][0]
__________________________________________________________________________________________________
batch_normalization_152 (BatchN (None, 61, 61, 32) 96 conv2d_183[0][0]
__________________________________________________________________________________________________
batch_normalization_154 (BatchN (None, 61, 61, 32) 96 conv2d_185[0][0]
__________________________________________________________________________________________________
batch_normalization_157 (BatchN (None, 61, 61, 64) 192 conv2d_188[0][0]
__________________________________________________________________________________________________
activation_152 (Activation) (None, 61, 61, 32) 0 batch_normalization_152[0][0]
__________________________________________________________________________________________________
activation_154 (Activation) (None, 61, 61, 32) 0 batch_normalization_154[0][0]
__________________________________________________________________________________________________
activation_157 (Activation) (None, 61, 61, 64) 0 batch_normalization_157[0][0]
__________________________________________________________________________________________________
block35_8_mixed (Concatenate) (None, 61, 61, 128) 0 activation_152[0][0]
activation_154[0][0]
activation_157[0][0]
__________________________________________________________________________________________________
block35_8_conv (Conv2D) (None, 61, 61, 320) 41280 block35_8_mixed[0][0]
__________________________________________________________________________________________________
block35_8 (Lambda) (None, 61, 61, 320) 0 block35_7_ac[0][0]
block35_8_conv[0][0]
__________________________________________________________________________________________________
block35_8_ac (Activation) (None, 61, 61, 320) 0 block35_8[0][0]
__________________________________________________________________________________________________
conv2d_192 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_161 (BatchN (None, 61, 61, 32) 96 conv2d_192[0][0]
__________________________________________________________________________________________________
activation_161 (Activation) (None, 61, 61, 32) 0 batch_normalization_161[0][0]
__________________________________________________________________________________________________
conv2d_190 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_193 (Conv2D) (None, 61, 61, 48) 13824 activation_161[0][0]
__________________________________________________________________________________________________
batch_normalization_159 (BatchN (None, 61, 61, 32) 96 conv2d_190[0][0]
__________________________________________________________________________________________________
batch_normalization_162 (BatchN (None, 61, 61, 48) 144 conv2d_193[0][0]
__________________________________________________________________________________________________
activation_159 (Activation) (None, 61, 61, 32) 0 batch_normalization_159[0][0]
__________________________________________________________________________________________________
activation_162 (Activation) (None, 61, 61, 48) 0 batch_normalization_162[0][0]
__________________________________________________________________________________________________
conv2d_189 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_191 (Conv2D) (None, 61, 61, 32) 9216 activation_159[0][0]
__________________________________________________________________________________________________
conv2d_194 (Conv2D) (None, 61, 61, 64) 27648 activation_162[0][0]
__________________________________________________________________________________________________
batch_normalization_158 (BatchN (None, 61, 61, 32) 96 conv2d_189[0][0]
__________________________________________________________________________________________________
batch_normalization_160 (BatchN (None, 61, 61, 32) 96 conv2d_191[0][0]
__________________________________________________________________________________________________
batch_normalization_163 (BatchN (None, 61, 61, 64) 192 conv2d_194[0][0]
__________________________________________________________________________________________________
activation_158 (Activation) (None, 61, 61, 32) 0 batch_normalization_158[0][0]
__________________________________________________________________________________________________
activation_160 (Activation) (None, 61, 61, 32) 0 batch_normalization_160[0][0]
__________________________________________________________________________________________________
activation_163 (Activation) (None, 61, 61, 64) 0 batch_normalization_163[0][0]
__________________________________________________________________________________________________
block35_9_mixed (Concatenate) (None, 61, 61, 128) 0 activation_158[0][0]
activation_160[0][0]
activation_163[0][0]
__________________________________________________________________________________________________
block35_9_conv (Conv2D) (None, 61, 61, 320) 41280 block35_9_mixed[0][0]
__________________________________________________________________________________________________
block35_9 (Lambda) (None, 61, 61, 320) 0 block35_8_ac[0][0]
block35_9_conv[0][0]
__________________________________________________________________________________________________
block35_9_ac (Activation) (None, 61, 61, 320) 0 block35_9[0][0]
__________________________________________________________________________________________________
conv2d_198 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_167 (BatchN (None, 61, 61, 32) 96 conv2d_198[0][0]
__________________________________________________________________________________________________
activation_167 (Activation) (None, 61, 61, 32) 0 batch_normalization_167[0][0]
__________________________________________________________________________________________________
conv2d_196 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_199 (Conv2D) (None, 61, 61, 48) 13824 activation_167[0][0]
__________________________________________________________________________________________________
batch_normalization_165 (BatchN (None, 61, 61, 32) 96 conv2d_196[0][0]
__________________________________________________________________________________________________
batch_normalization_168 (BatchN (None, 61, 61, 48) 144 conv2d_199[0][0]
__________________________________________________________________________________________________
activation_165 (Activation) (None, 61, 61, 32) 0 batch_normalization_165[0][0]
__________________________________________________________________________________________________
activation_168 (Activation) (None, 61, 61, 48) 0 batch_normalization_168[0][0]
__________________________________________________________________________________________________
conv2d_195 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_197 (Conv2D) (None, 61, 61, 32) 9216 activation_165[0][0]
__________________________________________________________________________________________________
conv2d_200 (Conv2D) (None, 61, 61, 64) 27648 activation_168[0][0]
__________________________________________________________________________________________________
batch_normalization_164 (BatchN (None, 61, 61, 32) 96 conv2d_195[0][0]
__________________________________________________________________________________________________
batch_normalization_166 (BatchN (None, 61, 61, 32) 96 conv2d_197[0][0]
__________________________________________________________________________________________________
batch_normalization_169 (BatchN (None, 61, 61, 64) 192 conv2d_200[0][0]
__________________________________________________________________________________________________
activation_164 (Activation) (None, 61, 61, 32) 0 batch_normalization_164[0][0]
__________________________________________________________________________________________________
activation_166 (Activation) (None, 61, 61, 32) 0 batch_normalization_166[0][0]
__________________________________________________________________________________________________
activation_169 (Activation) (None, 61, 61, 64) 0 batch_normalization_169[0][0]
__________________________________________________________________________________________________
block35_10_mixed (Concatenate) (None, 61, 61, 128) 0 activation_164[0][0]
activation_166[0][0]
activation_169[0][0]
__________________________________________________________________________________________________
block35_10_conv (Conv2D) (None, 61, 61, 320) 41280 block35_10_mixed[0][0]
__________________________________________________________________________________________________
block35_10 (Lambda) (None, 61, 61, 320) 0 block35_9_ac[0][0]
block35_10_conv[0][0]
__________________________________________________________________________________________________
block35_10_ac (Activation) (None, 61, 61, 320) 0 block35_10[0][0]
__________________________________________________________________________________________________
conv2d_202 (Conv2D) (None, 61, 61, 256) 81920 block35_10_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_171 (BatchN (None, 61, 61, 256) 768 conv2d_202[0][0]
__________________________________________________________________________________________________
activation_171 (Activation) (None, 61, 61, 256) 0 batch_normalization_171[0][0]
__________________________________________________________________________________________________
conv2d_203 (Conv2D) (None, 61, 61, 256) 589824 activation_171[0][0]
__________________________________________________________________________________________________
batch_normalization_172 (BatchN (None, 61, 61, 256) 768 conv2d_203[0][0]
__________________________________________________________________________________________________
activation_172 (Activation) (None, 61, 61, 256) 0 batch_normalization_172[0][0]
__________________________________________________________________________________________________
conv2d_201 (Conv2D) (None, 30, 30, 384) 1105920 block35_10_ac[0][0]
__________________________________________________________________________________________________
conv2d_204 (Conv2D) (None, 30, 30, 384) 884736 activation_172[0][0]
__________________________________________________________________________________________________
batch_normalization_170 (BatchN (None, 30, 30, 384) 1152 conv2d_201[0][0]
__________________________________________________________________________________________________
batch_normalization_173 (BatchN (None, 30, 30, 384) 1152 conv2d_204[0][0]
__________________________________________________________________________________________________
activation_170 (Activation) (None, 30, 30, 384) 0 batch_normalization_170[0][0]
__________________________________________________________________________________________________
activation_173 (Activation) (None, 30, 30, 384) 0 batch_normalization_173[0][0]
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 30, 30, 320) 0 block35_10_ac[0][0]
__________________________________________________________________________________________________
mixed_6a (Concatenate) (None, 30, 30, 1088) 0 activation_170[0][0]
activation_173[0][0]
max_pooling2d_16[0][0]
__________________________________________________________________________________________________
conv2d_206 (Conv2D) (None, 30, 30, 128) 139264 mixed_6a[0][0]
__________________________________________________________________________________________________
batch_normalization_175 (BatchN (None, 30, 30, 128) 384 conv2d_206[0][0]
__________________________________________________________________________________________________
activation_175 (Activation) (None, 30, 30, 128) 0 batch_normalization_175[0][0]
__________________________________________________________________________________________________
conv2d_207 (Conv2D) (None, 30, 30, 160) 143360 activation_175[0][0]
__________________________________________________________________________________________________
batch_normalization_176 (BatchN (None, 30, 30, 160) 480 conv2d_207[0][0]
__________________________________________________________________________________________________
activation_176 (Activation) (None, 30, 30, 160) 0 batch_normalization_176[0][0]
__________________________________________________________________________________________________
conv2d_205 (Conv2D) (None, 30, 30, 192) 208896 mixed_6a[0][0]
__________________________________________________________________________________________________
conv2d_208 (Conv2D) (None, 30, 30, 192) 215040 activation_176[0][0]
__________________________________________________________________________________________________
batch_normalization_174 (BatchN (None, 30, 30, 192) 576 conv2d_205[0][0]
__________________________________________________________________________________________________
batch_normalization_177 (BatchN (None, 30, 30, 192) 576 conv2d_208[0][0]
__________________________________________________________________________________________________
activation_174 (Activation) (None, 30, 30, 192) 0 batch_normalization_174[0][0]
__________________________________________________________________________________________________
activation_177 (Activation) (None, 30, 30, 192) 0 batch_normalization_177[0][0]
__________________________________________________________________________________________________
block17_1_mixed (Concatenate) (None, 30, 30, 384) 0 activation_174[0][0]
activation_177[0][0]
__________________________________________________________________________________________________
block17_1_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_1_mixed[0][0]
__________________________________________________________________________________________________
block17_1 (Lambda) (None, 30, 30, 1088) 0 mixed_6a[0][0]
block17_1_conv[0][0]
__________________________________________________________________________________________________
block17_1_ac (Activation) (None, 30, 30, 1088) 0 block17_1[0][0]
__________________________________________________________________________________________________
conv2d_210 (Conv2D) (None, 30, 30, 128) 139264 block17_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_179 (BatchN (None, 30, 30, 128) 384 conv2d_210[0][0]
__________________________________________________________________________________________________
activation_179 (Activation) (None, 30, 30, 128) 0 batch_normalization_179[0][0]
__________________________________________________________________________________________________
conv2d_211 (Conv2D) (None, 30, 30, 160) 143360 activation_179[0][0]
__________________________________________________________________________________________________
batch_normalization_180 (BatchN (None, 30, 30, 160) 480 conv2d_211[0][0]
__________________________________________________________________________________________________
activation_180 (Activation) (None, 30, 30, 160) 0 batch_normalization_180[0][0]
__________________________________________________________________________________________________
conv2d_209 (Conv2D) (None, 30, 30, 192) 208896 block17_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_212 (Conv2D) (None, 30, 30, 192) 215040 activation_180[0][0]
__________________________________________________________________________________________________
batch_normalization_178 (BatchN (None, 30, 30, 192) 576 conv2d_209[0][0]
__________________________________________________________________________________________________
batch_normalization_181 (BatchN (None, 30, 30, 192) 576 conv2d_212[0][0]
__________________________________________________________________________________________________
activation_178 (Activation) (None, 30, 30, 192) 0 batch_normalization_178[0][0]
__________________________________________________________________________________________________
activation_181 (Activation) (None, 30, 30, 192) 0 batch_normalization_181[0][0]
__________________________________________________________________________________________________
block17_2_mixed (Concatenate) (None, 30, 30, 384) 0 activation_178[0][0]
activation_181[0][0]
__________________________________________________________________________________________________
block17_2_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_2_mixed[0][0]
__________________________________________________________________________________________________
block17_2 (Lambda) (None, 30, 30, 1088) 0 block17_1_ac[0][0]
block17_2_conv[0][0]
__________________________________________________________________________________________________
block17_2_ac (Activation) (None, 30, 30, 1088) 0 block17_2[0][0]
__________________________________________________________________________________________________
conv2d_214 (Conv2D) (None, 30, 30, 128) 139264 block17_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_183 (BatchN (None, 30, 30, 128) 384 conv2d_214[0][0]
__________________________________________________________________________________________________
activation_183 (Activation) (None, 30, 30, 128) 0 batch_normalization_183[0][0]
__________________________________________________________________________________________________
conv2d_215 (Conv2D) (None, 30, 30, 160) 143360 activation_183[0][0]
__________________________________________________________________________________________________
batch_normalization_184 (BatchN (None, 30, 30, 160) 480 conv2d_215[0][0]
__________________________________________________________________________________________________
activation_184 (Activation) (None, 30, 30, 160) 0 batch_normalization_184[0][0]
__________________________________________________________________________________________________
conv2d_213 (Conv2D) (None, 30, 30, 192) 208896 block17_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_216 (Conv2D) (None, 30, 30, 192) 215040 activation_184[0][0]
__________________________________________________________________________________________________
batch_normalization_182 (BatchN (None, 30, 30, 192) 576 conv2d_213[0][0]
__________________________________________________________________________________________________
batch_normalization_185 (BatchN (None, 30, 30, 192) 576 conv2d_216[0][0]
__________________________________________________________________________________________________
activation_182 (Activation) (None, 30, 30, 192) 0 batch_normalization_182[0][0]
__________________________________________________________________________________________________
activation_185 (Activation) (None, 30, 30, 192) 0 batch_normalization_185[0][0]
__________________________________________________________________________________________________
block17_3_mixed (Concatenate) (None, 30, 30, 384) 0 activation_182[0][0]
activation_185[0][0]
__________________________________________________________________________________________________
block17_3_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_3_mixed[0][0]
__________________________________________________________________________________________________
block17_3 (Lambda) (None, 30, 30, 1088) 0 block17_2_ac[0][0]
block17_3_conv[0][0]
__________________________________________________________________________________________________
block17_3_ac (Activation) (None, 30, 30, 1088) 0 block17_3[0][0]
__________________________________________________________________________________________________
conv2d_218 (Conv2D) (None, 30, 30, 128) 139264 block17_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_187 (BatchN (None, 30, 30, 128) 384 conv2d_218[0][0]
__________________________________________________________________________________________________
activation_187 (Activation) (None, 30, 30, 128) 0 batch_normalization_187[0][0]
__________________________________________________________________________________________________
conv2d_219 (Conv2D) (None, 30, 30, 160) 143360 activation_187[0][0]
__________________________________________________________________________________________________
batch_normalization_188 (BatchN (None, 30, 30, 160) 480 conv2d_219[0][0]
__________________________________________________________________________________________________
activation_188 (Activation) (None, 30, 30, 160) 0 batch_normalization_188[0][0]
__________________________________________________________________________________________________
conv2d_217 (Conv2D) (None, 30, 30, 192) 208896 block17_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_220 (Conv2D) (None, 30, 30, 192) 215040 activation_188[0][0]
__________________________________________________________________________________________________
batch_normalization_186 (BatchN (None, 30, 30, 192) 576 conv2d_217[0][0]
__________________________________________________________________________________________________
batch_normalization_189 (BatchN (None, 30, 30, 192) 576 conv2d_220[0][0]
__________________________________________________________________________________________________
activation_186 (Activation) (None, 30, 30, 192) 0 batch_normalization_186[0][0]
__________________________________________________________________________________________________
activation_189 (Activation) (None, 30, 30, 192) 0 batch_normalization_189[0][0]
__________________________________________________________________________________________________
block17_4_mixed (Concatenate) (None, 30, 30, 384) 0 activation_186[0][0]
activation_189[0][0]
__________________________________________________________________________________________________
block17_4_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_4_mixed[0][0]
__________________________________________________________________________________________________
block17_4 (Lambda) (None, 30, 30, 1088) 0 block17_3_ac[0][0]
block17_4_conv[0][0]
__________________________________________________________________________________________________
block17_4_ac (Activation) (None, 30, 30, 1088) 0 block17_4[0][0]
__________________________________________________________________________________________________
conv2d_222 (Conv2D) (None, 30, 30, 128) 139264 block17_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_191 (BatchN (None, 30, 30, 128) 384 conv2d_222[0][0]
__________________________________________________________________________________________________
activation_191 (Activation) (None, 30, 30, 128) 0 batch_normalization_191[0][0]
__________________________________________________________________________________________________
conv2d_223 (Conv2D) (None, 30, 30, 160) 143360 activation_191[0][0]
__________________________________________________________________________________________________
batch_normalization_192 (BatchN (None, 30, 30, 160) 480 conv2d_223[0][0]
__________________________________________________________________________________________________
activation_192 (Activation) (None, 30, 30, 160) 0 batch_normalization_192[0][0]
__________________________________________________________________________________________________
conv2d_221 (Conv2D) (None, 30, 30, 192) 208896 block17_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_224 (Conv2D) (None, 30, 30, 192) 215040 activation_192[0][0]
__________________________________________________________________________________________________
batch_normalization_190 (BatchN (None, 30, 30, 192) 576 conv2d_221[0][0]
__________________________________________________________________________________________________
batch_normalization_193 (BatchN (None, 30, 30, 192) 576 conv2d_224[0][0]
__________________________________________________________________________________________________
activation_190 (Activation) (None, 30, 30, 192) 0 batch_normalization_190[0][0]
__________________________________________________________________________________________________
activation_193 (Activation) (None, 30, 30, 192) 0 batch_normalization_193[0][0]
__________________________________________________________________________________________________
block17_5_mixed (Concatenate) (None, 30, 30, 384) 0 activation_190[0][0]
activation_193[0][0]
__________________________________________________________________________________________________
block17_5_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_5_mixed[0][0]
__________________________________________________________________________________________________
block17_5 (Lambda) (None, 30, 30, 1088) 0 block17_4_ac[0][0]
block17_5_conv[0][0]
__________________________________________________________________________________________________
block17_5_ac (Activation) (None, 30, 30, 1088) 0 block17_5[0][0]
__________________________________________________________________________________________________
conv2d_226 (Conv2D) (None, 30, 30, 128) 139264 block17_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_195 (BatchN (None, 30, 30, 128) 384 conv2d_226[0][0]
__________________________________________________________________________________________________
activation_195 (Activation) (None, 30, 30, 128) 0 batch_normalization_195[0][0]
__________________________________________________________________________________________________
conv2d_227 (Conv2D) (None, 30, 30, 160) 143360 activation_195[0][0]
__________________________________________________________________________________________________
batch_normalization_196 (BatchN (None, 30, 30, 160) 480 conv2d_227[0][0]
__________________________________________________________________________________________________
activation_196 (Activation) (None, 30, 30, 160) 0 batch_normalization_196[0][0]
__________________________________________________________________________________________________
conv2d_225 (Conv2D) (None, 30, 30, 192) 208896 block17_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_228 (Conv2D) (None, 30, 30, 192) 215040 activation_196[0][0]
__________________________________________________________________________________________________
batch_normalization_194 (BatchN (None, 30, 30, 192) 576 conv2d_225[0][0]
__________________________________________________________________________________________________
batch_normalization_197 (BatchN (None, 30, 30, 192) 576 conv2d_228[0][0]
__________________________________________________________________________________________________
activation_194 (Activation) (None, 30, 30, 192) 0 batch_normalization_194[0][0]
__________________________________________________________________________________________________
activation_197 (Activation) (None, 30, 30, 192) 0 batch_normalization_197[0][0]
__________________________________________________________________________________________________
block17_6_mixed (Concatenate) (None, 30, 30, 384) 0 activation_194[0][0]
activation_197[0][0]
__________________________________________________________________________________________________
block17_6_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_6_mixed[0][0]
__________________________________________________________________________________________________
block17_6 (Lambda) (None, 30, 30, 1088) 0 block17_5_ac[0][0]
block17_6_conv[0][0]
__________________________________________________________________________________________________
block17_6_ac (Activation) (None, 30, 30, 1088) 0 block17_6[0][0]
__________________________________________________________________________________________________
conv2d_230 (Conv2D) (None, 30, 30, 128) 139264 block17_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_199 (BatchN (None, 30, 30, 128) 384 conv2d_230[0][0]
__________________________________________________________________________________________________
activation_199 (Activation) (None, 30, 30, 128) 0 batch_normalization_199[0][0]
__________________________________________________________________________________________________
conv2d_231 (Conv2D) (None, 30, 30, 160) 143360 activation_199[0][0]
__________________________________________________________________________________________________
batch_normalization_200 (BatchN (None, 30, 30, 160) 480 conv2d_231[0][0]
__________________________________________________________________________________________________
activation_200 (Activation) (None, 30, 30, 160) 0 batch_normalization_200[0][0]
__________________________________________________________________________________________________
conv2d_229 (Conv2D) (None, 30, 30, 192) 208896 block17_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_232 (Conv2D) (None, 30, 30, 192) 215040 activation_200[0][0]
__________________________________________________________________________________________________
batch_normalization_198 (BatchN (None, 30, 30, 192) 576 conv2d_229[0][0]
__________________________________________________________________________________________________
batch_normalization_201 (BatchN (None, 30, 30, 192) 576 conv2d_232[0][0]
__________________________________________________________________________________________________
activation_198 (Activation) (None, 30, 30, 192) 0 batch_normalization_198[0][0]
__________________________________________________________________________________________________
activation_201 (Activation) (None, 30, 30, 192) 0 batch_normalization_201[0][0]
__________________________________________________________________________________________________
block17_7_mixed (Concatenate) (None, 30, 30, 384) 0 activation_198[0][0]
activation_201[0][0]
__________________________________________________________________________________________________
block17_7_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_7_mixed[0][0]
__________________________________________________________________________________________________
block17_7 (Lambda) (None, 30, 30, 1088) 0 block17_6_ac[0][0]
block17_7_conv[0][0]
__________________________________________________________________________________________________
block17_7_ac (Activation) (None, 30, 30, 1088) 0 block17_7[0][0]
__________________________________________________________________________________________________
conv2d_234 (Conv2D) (None, 30, 30, 128) 139264 block17_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_203 (BatchN (None, 30, 30, 128) 384 conv2d_234[0][0]
__________________________________________________________________________________________________
activation_203 (Activation) (None, 30, 30, 128) 0 batch_normalization_203[0][0]
__________________________________________________________________________________________________
conv2d_235 (Conv2D) (None, 30, 30, 160) 143360 activation_203[0][0]
__________________________________________________________________________________________________
batch_normalization_204 (BatchN (None, 30, 30, 160) 480 conv2d_235[0][0]
__________________________________________________________________________________________________
activation_204 (Activation) (None, 30, 30, 160) 0 batch_normalization_204[0][0]
__________________________________________________________________________________________________
conv2d_233 (Conv2D) (None, 30, 30, 192) 208896 block17_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_236 (Conv2D) (None, 30, 30, 192) 215040 activation_204[0][0]
__________________________________________________________________________________________________
batch_normalization_202 (BatchN (None, 30, 30, 192) 576 conv2d_233[0][0]
__________________________________________________________________________________________________
batch_normalization_205 (BatchN (None, 30, 30, 192) 576 conv2d_236[0][0]
__________________________________________________________________________________________________
activation_202 (Activation) (None, 30, 30, 192) 0 batch_normalization_202[0][0]
__________________________________________________________________________________________________
activation_205 (Activation) (None, 30, 30, 192) 0 batch_normalization_205[0][0]
__________________________________________________________________________________________________
block17_8_mixed (Concatenate) (None, 30, 30, 384) 0 activation_202[0][0]
activation_205[0][0]
__________________________________________________________________________________________________
block17_8_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_8_mixed[0][0]
__________________________________________________________________________________________________
block17_8 (Lambda) (None, 30, 30, 1088) 0 block17_7_ac[0][0]
block17_8_conv[0][0]
__________________________________________________________________________________________________
block17_8_ac (Activation) (None, 30, 30, 1088) 0 block17_8[0][0]
__________________________________________________________________________________________________
conv2d_238 (Conv2D) (None, 30, 30, 128) 139264 block17_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_207 (BatchN (None, 30, 30, 128) 384 conv2d_238[0][0]
__________________________________________________________________________________________________
activation_207 (Activation) (None, 30, 30, 128) 0 batch_normalization_207[0][0]
__________________________________________________________________________________________________
conv2d_239 (Conv2D) (None, 30, 30, 160) 143360 activation_207[0][0]
__________________________________________________________________________________________________
batch_normalization_208 (BatchN (None, 30, 30, 160) 480 conv2d_239[0][0]
__________________________________________________________________________________________________
activation_208 (Activation) (None, 30, 30, 160) 0 batch_normalization_208[0][0]
__________________________________________________________________________________________________
conv2d_237 (Conv2D) (None, 30, 30, 192) 208896 block17_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_240 (Conv2D) (None, 30, 30, 192) 215040 activation_208[0][0]
__________________________________________________________________________________________________
batch_normalization_206 (BatchN (None, 30, 30, 192) 576 conv2d_237[0][0]
__________________________________________________________________________________________________
batch_normalization_209 (BatchN (None, 30, 30, 192) 576 conv2d_240[0][0]
__________________________________________________________________________________________________
activation_206 (Activation) (None, 30, 30, 192) 0 batch_normalization_206[0][0]
__________________________________________________________________________________________________
activation_209 (Activation) (None, 30, 30, 192) 0 batch_normalization_209[0][0]
__________________________________________________________________________________________________
block17_9_mixed (Concatenate) (None, 30, 30, 384) 0 activation_206[0][0]
activation_209[0][0]
__________________________________________________________________________________________________
block17_9_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_9_mixed[0][0]
__________________________________________________________________________________________________
block17_9 (Lambda) (None, 30, 30, 1088) 0 block17_8_ac[0][0]
block17_9_conv[0][0]
__________________________________________________________________________________________________
block17_9_ac (Activation) (None, 30, 30, 1088) 0 block17_9[0][0]
__________________________________________________________________________________________________
conv2d_242 (Conv2D) (None, 30, 30, 128) 139264 block17_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_211 (BatchN (None, 30, 30, 128) 384 conv2d_242[0][0]
__________________________________________________________________________________________________
activation_211 (Activation) (None, 30, 30, 128) 0 batch_normalization_211[0][0]
__________________________________________________________________________________________________
conv2d_243 (Conv2D) (None, 30, 30, 160) 143360 activation_211[0][0]
__________________________________________________________________________________________________
batch_normalization_212 (BatchN (None, 30, 30, 160) 480 conv2d_243[0][0]
__________________________________________________________________________________________________
activation_212 (Activation) (None, 30, 30, 160) 0 batch_normalization_212[0][0]
__________________________________________________________________________________________________
conv2d_241 (Conv2D) (None, 30, 30, 192) 208896 block17_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_244 (Conv2D) (None, 30, 30, 192) 215040 activation_212[0][0]
__________________________________________________________________________________________________
batch_normalization_210 (BatchN (None, 30, 30, 192) 576 conv2d_241[0][0]
__________________________________________________________________________________________________
batch_normalization_213 (BatchN (None, 30, 30, 192) 576 conv2d_244[0][0]
__________________________________________________________________________________________________
activation_210 (Activation) (None, 30, 30, 192) 0 batch_normalization_210[0][0]
__________________________________________________________________________________________________
activation_213 (Activation) (None, 30, 30, 192) 0 batch_normalization_213[0][0]
__________________________________________________________________________________________________
block17_10_mixed (Concatenate) (None, 30, 30, 384) 0 activation_210[0][0]
activation_213[0][0]
__________________________________________________________________________________________________
block17_10_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_10_mixed[0][0]
__________________________________________________________________________________________________
block17_10 (Lambda) (None, 30, 30, 1088) 0 block17_9_ac[0][0]
block17_10_conv[0][0]
__________________________________________________________________________________________________
block17_10_ac (Activation) (None, 30, 30, 1088) 0 block17_10[0][0]
__________________________________________________________________________________________________
conv2d_246 (Conv2D) (None, 30, 30, 128) 139264 block17_10_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_215 (BatchN (None, 30, 30, 128) 384 conv2d_246[0][0]
__________________________________________________________________________________________________
activation_215 (Activation) (None, 30, 30, 128) 0 batch_normalization_215[0][0]
__________________________________________________________________________________________________
conv2d_247 (Conv2D) (None, 30, 30, 160) 143360 activation_215[0][0]
__________________________________________________________________________________________________
batch_normalization_216 (BatchN (None, 30, 30, 160) 480 conv2d_247[0][0]
__________________________________________________________________________________________________
activation_216 (Activation) (None, 30, 30, 160) 0 batch_normalization_216[0][0]
__________________________________________________________________________________________________
conv2d_245 (Conv2D) (None, 30, 30, 192) 208896 block17_10_ac[0][0]
__________________________________________________________________________________________________
conv2d_248 (Conv2D) (None, 30, 30, 192) 215040 activation_216[0][0]
__________________________________________________________________________________________________
batch_normalization_214 (BatchN (None, 30, 30, 192) 576 conv2d_245[0][0]
__________________________________________________________________________________________________
batch_normalization_217 (BatchN (None, 30, 30, 192) 576 conv2d_248[0][0]
__________________________________________________________________________________________________
activation_214 (Activation) (None, 30, 30, 192) 0 batch_normalization_214[0][0]
__________________________________________________________________________________________________
activation_217 (Activation) (None, 30, 30, 192) 0 batch_normalization_217[0][0]
__________________________________________________________________________________________________
block17_11_mixed (Concatenate) (None, 30, 30, 384) 0 activation_214[0][0]
activation_217[0][0]
__________________________________________________________________________________________________
block17_11_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_11_mixed[0][0]
__________________________________________________________________________________________________
block17_11 (Lambda) (None, 30, 30, 1088) 0 block17_10_ac[0][0]
block17_11_conv[0][0]
__________________________________________________________________________________________________
block17_11_ac (Activation) (None, 30, 30, 1088) 0 block17_11[0][0]
__________________________________________________________________________________________________
conv2d_250 (Conv2D) (None, 30, 30, 128) 139264 block17_11_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_219 (BatchN (None, 30, 30, 128) 384 conv2d_250[0][0]
__________________________________________________________________________________________________
activation_219 (Activation) (None, 30, 30, 128) 0 batch_normalization_219[0][0]
__________________________________________________________________________________________________
conv2d_251 (Conv2D) (None, 30, 30, 160) 143360 activation_219[0][0]
__________________________________________________________________________________________________
batch_normalization_220 (BatchN (None, 30, 30, 160) 480 conv2d_251[0][0]
__________________________________________________________________________________________________
activation_220 (Activation) (None, 30, 30, 160) 0 batch_normalization_220[0][0]
__________________________________________________________________________________________________
conv2d_249 (Conv2D) (None, 30, 30, 192) 208896 block17_11_ac[0][0]
__________________________________________________________________________________________________
conv2d_252 (Conv2D) (None, 30, 30, 192) 215040 activation_220[0][0]
__________________________________________________________________________________________________
batch_normalization_218 (BatchN (None, 30, 30, 192) 576 conv2d_249[0][0]
__________________________________________________________________________________________________
batch_normalization_221 (BatchN (None, 30, 30, 192) 576 conv2d_252[0][0]
__________________________________________________________________________________________________
activation_218 (Activation) (None, 30, 30, 192) 0 batch_normalization_218[0][0]
__________________________________________________________________________________________________
activation_221 (Activation) (None, 30, 30, 192) 0 batch_normalization_221[0][0]
__________________________________________________________________________________________________
block17_12_mixed (Concatenate) (None, 30, 30, 384) 0 activation_218[0][0]
activation_221[0][0]
__________________________________________________________________________________________________
block17_12_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_12_mixed[0][0]
__________________________________________________________________________________________________
block17_12 (Lambda) (None, 30, 30, 1088) 0 block17_11_ac[0][0]
block17_12_conv[0][0]
__________________________________________________________________________________________________
block17_12_ac (Activation) (None, 30, 30, 1088) 0 block17_12[0][0]
__________________________________________________________________________________________________
conv2d_254 (Conv2D) (None, 30, 30, 128) 139264 block17_12_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_223 (BatchN (None, 30, 30, 128) 384 conv2d_254[0][0]
__________________________________________________________________________________________________
activation_223 (Activation) (None, 30, 30, 128) 0 batch_normalization_223[0][0]
__________________________________________________________________________________________________
conv2d_255 (Conv2D) (None, 30, 30, 160) 143360 activation_223[0][0]
__________________________________________________________________________________________________
batch_normalization_224 (BatchN (None, 30, 30, 160) 480 conv2d_255[0][0]
__________________________________________________________________________________________________
activation_224 (Activation) (None, 30, 30, 160) 0 batch_normalization_224[0][0]
__________________________________________________________________________________________________
conv2d_253 (Conv2D) (None, 30, 30, 192) 208896 block17_12_ac[0][0]
__________________________________________________________________________________________________
conv2d_256 (Conv2D) (None, 30, 30, 192) 215040 activation_224[0][0]
__________________________________________________________________________________________________
batch_normalization_222 (BatchN (None, 30, 30, 192) 576 conv2d_253[0][0]
__________________________________________________________________________________________________
batch_normalization_225 (BatchN (None, 30, 30, 192) 576 conv2d_256[0][0]
__________________________________________________________________________________________________
activation_222 (Activation) (None, 30, 30, 192) 0 batch_normalization_222[0][0]
__________________________________________________________________________________________________
activation_225 (Activation) (None, 30, 30, 192) 0 batch_normalization_225[0][0]
__________________________________________________________________________________________________
block17_13_mixed (Concatenate) (None, 30, 30, 384) 0 activation_222[0][0]
activation_225[0][0]
__________________________________________________________________________________________________
block17_13_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_13_mixed[0][0]
__________________________________________________________________________________________________
block17_13 (Lambda) (None, 30, 30, 1088) 0 block17_12_ac[0][0]
block17_13_conv[0][0]
__________________________________________________________________________________________________
block17_13_ac (Activation) (None, 30, 30, 1088) 0 block17_13[0][0]
__________________________________________________________________________________________________
conv2d_258 (Conv2D) (None, 30, 30, 128) 139264 block17_13_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_227 (BatchN (None, 30, 30, 128) 384 conv2d_258[0][0]
__________________________________________________________________________________________________
activation_227 (Activation) (None, 30, 30, 128) 0 batch_normalization_227[0][0]
__________________________________________________________________________________________________
conv2d_259 (Conv2D) (None, 30, 30, 160) 143360 activation_227[0][0]
__________________________________________________________________________________________________
batch_normalization_228 (BatchN (None, 30, 30, 160) 480 conv2d_259[0][0]
__________________________________________________________________________________________________
activation_228 (Activation) (None, 30, 30, 160) 0 batch_normalization_228[0][0]
__________________________________________________________________________________________________
conv2d_257 (Conv2D) (None, 30, 30, 192) 208896 block17_13_ac[0][0]
__________________________________________________________________________________________________
conv2d_260 (Conv2D) (None, 30, 30, 192) 215040 activation_228[0][0]
__________________________________________________________________________________________________
batch_normalization_226 (BatchN (None, 30, 30, 192) 576 conv2d_257[0][0]
__________________________________________________________________________________________________
batch_normalization_229 (BatchN (None, 30, 30, 192) 576 conv2d_260[0][0]
__________________________________________________________________________________________________
activation_226 (Activation) (None, 30, 30, 192) 0 batch_normalization_226[0][0]
__________________________________________________________________________________________________
activation_229 (Activation) (None, 30, 30, 192) 0 batch_normalization_229[0][0]
__________________________________________________________________________________________________
block17_14_mixed (Concatenate) (None, 30, 30, 384) 0 activation_226[0][0]
activation_229[0][0]
__________________________________________________________________________________________________
block17_14_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_14_mixed[0][0]
__________________________________________________________________________________________________
block17_14 (Lambda) (None, 30, 30, 1088) 0 block17_13_ac[0][0]
block17_14_conv[0][0]
__________________________________________________________________________________________________
block17_14_ac (Activation) (None, 30, 30, 1088) 0 block17_14[0][0]
__________________________________________________________________________________________________
conv2d_262 (Conv2D) (None, 30, 30, 128) 139264 block17_14_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_231 (BatchN (None, 30, 30, 128) 384 conv2d_262[0][0]
__________________________________________________________________________________________________
activation_231 (Activation) (None, 30, 30, 128) 0 batch_normalization_231[0][0]
__________________________________________________________________________________________________
conv2d_263 (Conv2D) (None, 30, 30, 160) 143360 activation_231[0][0]
__________________________________________________________________________________________________
batch_normalization_232 (BatchN (None, 30, 30, 160) 480 conv2d_263[0][0]
__________________________________________________________________________________________________
activation_232 (Activation) (None, 30, 30, 160) 0 batch_normalization_232[0][0]
__________________________________________________________________________________________________
conv2d_261 (Conv2D) (None, 30, 30, 192) 208896 block17_14_ac[0][0]
__________________________________________________________________________________________________
conv2d_264 (Conv2D) (None, 30, 30, 192) 215040 activation_232[0][0]
__________________________________________________________________________________________________
batch_normalization_230 (BatchN (None, 30, 30, 192) 576 conv2d_261[0][0]
__________________________________________________________________________________________________
batch_normalization_233 (BatchN (None, 30, 30, 192) 576 conv2d_264[0][0]
__________________________________________________________________________________________________
activation_230 (Activation) (None, 30, 30, 192) 0 batch_normalization_230[0][0]
__________________________________________________________________________________________________
activation_233 (Activation) (None, 30, 30, 192) 0 batch_normalization_233[0][0]
__________________________________________________________________________________________________
block17_15_mixed (Concatenate) (None, 30, 30, 384) 0 activation_230[0][0]
activation_233[0][0]
__________________________________________________________________________________________________
block17_15_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_15_mixed[0][0]
__________________________________________________________________________________________________
block17_15 (Lambda) (None, 30, 30, 1088) 0 block17_14_ac[0][0]
block17_15_conv[0][0]
__________________________________________________________________________________________________
block17_15_ac (Activation) (None, 30, 30, 1088) 0 block17_15[0][0]
__________________________________________________________________________________________________
conv2d_266 (Conv2D) (None, 30, 30, 128) 139264 block17_15_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_235 (BatchN (None, 30, 30, 128) 384 conv2d_266[0][0]
__________________________________________________________________________________________________
activation_235 (Activation) (None, 30, 30, 128) 0 batch_normalization_235[0][0]
__________________________________________________________________________________________________
conv2d_267 (Conv2D) (None, 30, 30, 160) 143360 activation_235[0][0]
__________________________________________________________________________________________________
batch_normalization_236 (BatchN (None, 30, 30, 160) 480 conv2d_267[0][0]
__________________________________________________________________________________________________
activation_236 (Activation) (None, 30, 30, 160) 0 batch_normalization_236[0][0]
__________________________________________________________________________________________________
conv2d_265 (Conv2D) (None, 30, 30, 192) 208896 block17_15_ac[0][0]
__________________________________________________________________________________________________
conv2d_268 (Conv2D) (None, 30, 30, 192) 215040 activation_236[0][0]
__________________________________________________________________________________________________
batch_normalization_234 (BatchN (None, 30, 30, 192) 576 conv2d_265[0][0]
__________________________________________________________________________________________________
batch_normalization_237 (BatchN (None, 30, 30, 192) 576 conv2d_268[0][0]
__________________________________________________________________________________________________
activation_234 (Activation) (None, 30, 30, 192) 0 batch_normalization_234[0][0]
__________________________________________________________________________________________________
activation_237 (Activation) (None, 30, 30, 192) 0 batch_normalization_237[0][0]
__________________________________________________________________________________________________
block17_16_mixed (Concatenate) (None, 30, 30, 384) 0 activation_234[0][0]
activation_237[0][0]
__________________________________________________________________________________________________
block17_16_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_16_mixed[0][0]
__________________________________________________________________________________________________
block17_16 (Lambda) (None, 30, 30, 1088) 0 block17_15_ac[0][0]
block17_16_conv[0][0]
__________________________________________________________________________________________________
block17_16_ac (Activation) (None, 30, 30, 1088) 0 block17_16[0][0]
__________________________________________________________________________________________________
conv2d_270 (Conv2D) (None, 30, 30, 128) 139264 block17_16_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_239 (BatchN (None, 30, 30, 128) 384 conv2d_270[0][0]
__________________________________________________________________________________________________
activation_239 (Activation) (None, 30, 30, 128) 0 batch_normalization_239[0][0]
__________________________________________________________________________________________________
conv2d_271 (Conv2D) (None, 30, 30, 160) 143360 activation_239[0][0]
__________________________________________________________________________________________________
batch_normalization_240 (BatchN (None, 30, 30, 160) 480 conv2d_271[0][0]
__________________________________________________________________________________________________
activation_240 (Activation) (None, 30, 30, 160) 0 batch_normalization_240[0][0]
__________________________________________________________________________________________________
conv2d_269 (Conv2D) (None, 30, 30, 192) 208896 block17_16_ac[0][0]
__________________________________________________________________________________________________
conv2d_272 (Conv2D) (None, 30, 30, 192) 215040 activation_240[0][0]
__________________________________________________________________________________________________
batch_normalization_238 (BatchN (None, 30, 30, 192) 576 conv2d_269[0][0]
__________________________________________________________________________________________________
batch_normalization_241 (BatchN (None, 30, 30, 192) 576 conv2d_272[0][0]
__________________________________________________________________________________________________
activation_238 (Activation) (None, 30, 30, 192) 0 batch_normalization_238[0][0]
__________________________________________________________________________________________________
activation_241 (Activation) (None, 30, 30, 192) 0 batch_normalization_241[0][0]
__________________________________________________________________________________________________
block17_17_mixed (Concatenate) (None, 30, 30, 384) 0 activation_238[0][0]
activation_241[0][0]
__________________________________________________________________________________________________
block17_17_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_17_mixed[0][0]
__________________________________________________________________________________________________
block17_17 (Lambda) (None, 30, 30, 1088) 0 block17_16_ac[0][0]
block17_17_conv[0][0]
__________________________________________________________________________________________________
block17_17_ac (Activation) (None, 30, 30, 1088) 0 block17_17[0][0]
__________________________________________________________________________________________________
conv2d_274 (Conv2D) (None, 30, 30, 128) 139264 block17_17_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_243 (BatchN (None, 30, 30, 128) 384 conv2d_274[0][0]
__________________________________________________________________________________________________
activation_243 (Activation) (None, 30, 30, 128) 0 batch_normalization_243[0][0]
__________________________________________________________________________________________________
conv2d_275 (Conv2D) (None, 30, 30, 160) 143360 activation_243[0][0]
__________________________________________________________________________________________________
batch_normalization_244 (BatchN (None, 30, 30, 160) 480 conv2d_275[0][0]
__________________________________________________________________________________________________
activation_244 (Activation) (None, 30, 30, 160) 0 batch_normalization_244[0][0]
__________________________________________________________________________________________________
conv2d_273 (Conv2D) (None, 30, 30, 192) 208896 block17_17_ac[0][0]
__________________________________________________________________________________________________
conv2d_276 (Conv2D) (None, 30, 30, 192) 215040 activation_244[0][0]
__________________________________________________________________________________________________
batch_normalization_242 (BatchN (None, 30, 30, 192) 576 conv2d_273[0][0]
__________________________________________________________________________________________________
batch_normalization_245 (BatchN (None, 30, 30, 192) 576 conv2d_276[0][0]
__________________________________________________________________________________________________
activation_242 (Activation) (None, 30, 30, 192) 0 batch_normalization_242[0][0]
__________________________________________________________________________________________________
activation_245 (Activation) (None, 30, 30, 192) 0 batch_normalization_245[0][0]
__________________________________________________________________________________________________
block17_18_mixed (Concatenate) (None, 30, 30, 384) 0 activation_242[0][0]
activation_245[0][0]
__________________________________________________________________________________________________
block17_18_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_18_mixed[0][0]
__________________________________________________________________________________________________
block17_18 (Lambda) (None, 30, 30, 1088) 0 block17_17_ac[0][0]
block17_18_conv[0][0]
__________________________________________________________________________________________________
block17_18_ac (Activation) (None, 30, 30, 1088) 0 block17_18[0][0]
__________________________________________________________________________________________________
conv2d_278 (Conv2D) (None, 30, 30, 128) 139264 block17_18_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_247 (BatchN (None, 30, 30, 128) 384 conv2d_278[0][0]
__________________________________________________________________________________________________
activation_247 (Activation) (None, 30, 30, 128) 0 batch_normalization_247[0][0]
__________________________________________________________________________________________________
conv2d_279 (Conv2D) (None, 30, 30, 160) 143360 activation_247[0][0]
__________________________________________________________________________________________________
batch_normalization_248 (BatchN (None, 30, 30, 160) 480 conv2d_279[0][0]
__________________________________________________________________________________________________
activation_248 (Activation) (None, 30, 30, 160) 0 batch_normalization_248[0][0]
__________________________________________________________________________________________________
conv2d_277 (Conv2D) (None, 30, 30, 192) 208896 block17_18_ac[0][0]
__________________________________________________________________________________________________
conv2d_280 (Conv2D) (None, 30, 30, 192) 215040 activation_248[0][0]
__________________________________________________________________________________________________
batch_normalization_246 (BatchN (None, 30, 30, 192) 576 conv2d_277[0][0]
__________________________________________________________________________________________________
batch_normalization_249 (BatchN (None, 30, 30, 192) 576 conv2d_280[0][0]
__________________________________________________________________________________________________
activation_246 (Activation) (None, 30, 30, 192) 0 batch_normalization_246[0][0]
__________________________________________________________________________________________________
activation_249 (Activation) (None, 30, 30, 192) 0 batch_normalization_249[0][0]
__________________________________________________________________________________________________
block17_19_mixed (Concatenate) (None, 30, 30, 384) 0 activation_246[0][0]
activation_249[0][0]
__________________________________________________________________________________________________
block17_19_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_19_mixed[0][0]
__________________________________________________________________________________________________
block17_19 (Lambda) (None, 30, 30, 1088) 0 block17_18_ac[0][0]
block17_19_conv[0][0]
__________________________________________________________________________________________________
block17_19_ac (Activation) (None, 30, 30, 1088) 0 block17_19[0][0]
__________________________________________________________________________________________________
conv2d_282 (Conv2D) (None, 30, 30, 128) 139264 block17_19_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_251 (BatchN (None, 30, 30, 128) 384 conv2d_282[0][0]
__________________________________________________________________________________________________
activation_251 (Activation) (None, 30, 30, 128) 0 batch_normalization_251[0][0]
__________________________________________________________________________________________________
conv2d_283 (Conv2D) (None, 30, 30, 160) 143360 activation_251[0][0]
__________________________________________________________________________________________________
batch_normalization_252 (BatchN (None, 30, 30, 160) 480 conv2d_283[0][0]
__________________________________________________________________________________________________
activation_252 (Activation) (None, 30, 30, 160) 0 batch_normalization_252[0][0]
__________________________________________________________________________________________________
conv2d_281 (Conv2D) (None, 30, 30, 192) 208896 block17_19_ac[0][0]
__________________________________________________________________________________________________
conv2d_284 (Conv2D) (None, 30, 30, 192) 215040 activation_252[0][0]
__________________________________________________________________________________________________
batch_normalization_250 (BatchN (None, 30, 30, 192) 576 conv2d_281[0][0]
__________________________________________________________________________________________________
batch_normalization_253 (BatchN (None, 30, 30, 192) 576 conv2d_284[0][0]
__________________________________________________________________________________________________
activation_250 (Activation) (None, 30, 30, 192) 0 batch_normalization_250[0][0]
__________________________________________________________________________________________________
activation_253 (Activation) (None, 30, 30, 192) 0 batch_normalization_253[0][0]
__________________________________________________________________________________________________
block17_20_mixed (Concatenate) (None, 30, 30, 384) 0 activation_250[0][0]
activation_253[0][0]
__________________________________________________________________________________________________
block17_20_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_20_mixed[0][0]
__________________________________________________________________________________________________
block17_20 (Lambda) (None, 30, 30, 1088) 0 block17_19_ac[0][0]
block17_20_conv[0][0]
__________________________________________________________________________________________________
block17_20_ac (Activation) (None, 30, 30, 1088) 0 block17_20[0][0]
__________________________________________________________________________________________________
conv2d_289 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_258 (BatchN (None, 30, 30, 256) 768 conv2d_289[0][0]
__________________________________________________________________________________________________
activation_258 (Activation) (None, 30, 30, 256) 0 batch_normalization_258[0][0]
__________________________________________________________________________________________________
conv2d_285 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
conv2d_287 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
conv2d_290 (Conv2D) (None, 30, 30, 288) 663552 activation_258[0][0]
__________________________________________________________________________________________________
batch_normalization_254 (BatchN (None, 30, 30, 256) 768 conv2d_285[0][0]
__________________________________________________________________________________________________
batch_normalization_256 (BatchN (None, 30, 30, 256) 768 conv2d_287[0][0]
__________________________________________________________________________________________________
batch_normalization_259 (BatchN (None, 30, 30, 288) 864 conv2d_290[0][0]
__________________________________________________________________________________________________
activation_254 (Activation) (None, 30, 30, 256) 0 batch_normalization_254[0][0]
__________________________________________________________________________________________________
activation_256 (Activation) (None, 30, 30, 256) 0 batch_normalization_256[0][0]
__________________________________________________________________________________________________
activation_259 (Activation) (None, 30, 30, 288) 0 batch_normalization_259[0][0]
__________________________________________________________________________________________________
conv2d_286 (Conv2D) (None, 14, 14, 384) 884736 activation_254[0][0]
__________________________________________________________________________________________________
conv2d_288 (Conv2D) (None, 14, 14, 288) 663552 activation_256[0][0]
__________________________________________________________________________________________________
conv2d_291 (Conv2D) (None, 14, 14, 320) 829440 activation_259[0][0]
__________________________________________________________________________________________________
batch_normalization_255 (BatchN (None, 14, 14, 384) 1152 conv2d_286[0][0]
__________________________________________________________________________________________________
batch_normalization_257 (BatchN (None, 14, 14, 288) 864 conv2d_288[0][0]
__________________________________________________________________________________________________
batch_normalization_260 (BatchN (None, 14, 14, 320) 960 conv2d_291[0][0]
__________________________________________________________________________________________________
activation_255 (Activation) (None, 14, 14, 384) 0 batch_normalization_255[0][0]
__________________________________________________________________________________________________
activation_257 (Activation) (None, 14, 14, 288) 0 batch_normalization_257[0][0]
__________________________________________________________________________________________________
activation_260 (Activation) (None, 14, 14, 320) 0 batch_normalization_260[0][0]
__________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 14, 14, 1088) 0 block17_20_ac[0][0]
__________________________________________________________________________________________________
mixed_7a (Concatenate) (None, 14, 14, 2080) 0 activation_255[0][0]
activation_257[0][0]
activation_260[0][0]
max_pooling2d_17[0][0]
__________________________________________________________________________________________________
conv2d_293 (Conv2D) (None, 14, 14, 192) 399360 mixed_7a[0][0]
__________________________________________________________________________________________________
batch_normalization_262 (BatchN (None, 14, 14, 192) 576 conv2d_293[0][0]
__________________________________________________________________________________________________
activation_262 (Activation) (None, 14, 14, 192) 0 batch_normalization_262[0][0]
__________________________________________________________________________________________________
conv2d_294 (Conv2D) (None, 14, 14, 224) 129024 activation_262[0][0]
__________________________________________________________________________________________________
batch_normalization_263 (BatchN (None, 14, 14, 224) 672 conv2d_294[0][0]
__________________________________________________________________________________________________
activation_263 (Activation) (None, 14, 14, 224) 0 batch_normalization_263[0][0]
__________________________________________________________________________________________________
conv2d_292 (Conv2D) (None, 14, 14, 192) 399360 mixed_7a[0][0]
__________________________________________________________________________________________________
conv2d_295 (Conv2D) (None, 14, 14, 256) 172032 activation_263[0][0]
__________________________________________________________________________________________________
batch_normalization_261 (BatchN (None, 14, 14, 192) 576 conv2d_292[0][0]
__________________________________________________________________________________________________
batch_normalization_264 (BatchN (None, 14, 14, 256) 768 conv2d_295[0][0]
__________________________________________________________________________________________________
activation_261 (Activation) (None, 14, 14, 192) 0 batch_normalization_261[0][0]
__________________________________________________________________________________________________
activation_264 (Activation) (None, 14, 14, 256) 0 batch_normalization_264[0][0]
__________________________________________________________________________________________________
block8_1_mixed (Concatenate) (None, 14, 14, 448) 0 activation_261[0][0]
activation_264[0][0]
__________________________________________________________________________________________________
block8_1_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_1_mixed[0][0]
__________________________________________________________________________________________________
block8_1 (Lambda) (None, 14, 14, 2080) 0 mixed_7a[0][0]
block8_1_conv[0][0]
__________________________________________________________________________________________________
block8_1_ac (Activation) (None, 14, 14, 2080) 0 block8_1[0][0]
__________________________________________________________________________________________________
conv2d_297 (Conv2D) (None, 14, 14, 192) 399360 block8_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_266 (BatchN (None, 14, 14, 192) 576 conv2d_297[0][0]
__________________________________________________________________________________________________
activation_266 (Activation) (None, 14, 14, 192) 0 batch_normalization_266[0][0]
__________________________________________________________________________________________________
conv2d_298 (Conv2D) (None, 14, 14, 224) 129024 activation_266[0][0]
__________________________________________________________________________________________________
batch_normalization_267 (BatchN (None, 14, 14, 224) 672 conv2d_298[0][0]
__________________________________________________________________________________________________
activation_267 (Activation) (None, 14, 14, 224) 0 batch_normalization_267[0][0]
__________________________________________________________________________________________________
conv2d_296 (Conv2D) (None, 14, 14, 192) 399360 block8_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_299 (Conv2D) (None, 14, 14, 256) 172032 activation_267[0][0]
__________________________________________________________________________________________________
batch_normalization_265 (BatchN (None, 14, 14, 192) 576 conv2d_296[0][0]
__________________________________________________________________________________________________
batch_normalization_268 (BatchN (None, 14, 14, 256) 768 conv2d_299[0][0]
__________________________________________________________________________________________________
activation_265 (Activation) (None, 14, 14, 192) 0 batch_normalization_265[0][0]
__________________________________________________________________________________________________
activation_268 (Activation) (None, 14, 14, 256) 0 batch_normalization_268[0][0]
__________________________________________________________________________________________________
block8_2_mixed (Concatenate) (None, 14, 14, 448) 0 activation_265[0][0]
activation_268[0][0]
__________________________________________________________________________________________________
block8_2_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_2_mixed[0][0]
__________________________________________________________________________________________________
block8_2 (Lambda) (None, 14, 14, 2080) 0 block8_1_ac[0][0]
block8_2_conv[0][0]
__________________________________________________________________________________________________
block8_2_ac (Activation) (None, 14, 14, 2080) 0 block8_2[0][0]
__________________________________________________________________________________________________
conv2d_301 (Conv2D) (None, 14, 14, 192) 399360 block8_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_270 (BatchN (None, 14, 14, 192) 576 conv2d_301[0][0]
__________________________________________________________________________________________________
activation_270 (Activation) (None, 14, 14, 192) 0 batch_normalization_270[0][0]
__________________________________________________________________________________________________
conv2d_302 (Conv2D) (None, 14, 14, 224) 129024 activation_270[0][0]
__________________________________________________________________________________________________
batch_normalization_271 (BatchN (None, 14, 14, 224) 672 conv2d_302[0][0]
__________________________________________________________________________________________________
activation_271 (Activation) (None, 14, 14, 224) 0 batch_normalization_271[0][0]
__________________________________________________________________________________________________
conv2d_300 (Conv2D) (None, 14, 14, 192) 399360 block8_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_303 (Conv2D) (None, 14, 14, 256) 172032 activation_271[0][0]
__________________________________________________________________________________________________
batch_normalization_269 (BatchN (None, 14, 14, 192) 576 conv2d_300[0][0]
__________________________________________________________________________________________________
batch_normalization_272 (BatchN (None, 14, 14, 256) 768 conv2d_303[0][0]
__________________________________________________________________________________________________
activation_269 (Activation) (None, 14, 14, 192) 0 batch_normalization_269[0][0]
__________________________________________________________________________________________________
activation_272 (Activation) (None, 14, 14, 256) 0 batch_normalization_272[0][0]
__________________________________________________________________________________________________
block8_3_mixed (Concatenate) (None, 14, 14, 448) 0 activation_269[0][0]
activation_272[0][0]
__________________________________________________________________________________________________
block8_3_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_3_mixed[0][0]
__________________________________________________________________________________________________
block8_3 (Lambda) (None, 14, 14, 2080) 0 block8_2_ac[0][0]
block8_3_conv[0][0]
__________________________________________________________________________________________________
block8_3_ac (Activation) (None, 14, 14, 2080) 0 block8_3[0][0]
__________________________________________________________________________________________________
conv2d_305 (Conv2D) (None, 14, 14, 192) 399360 block8_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_274 (BatchN (None, 14, 14, 192) 576 conv2d_305[0][0]
__________________________________________________________________________________________________
activation_274 (Activation) (None, 14, 14, 192) 0 batch_normalization_274[0][0]
__________________________________________________________________________________________________
conv2d_306 (Conv2D) (None, 14, 14, 224) 129024 activation_274[0][0]
__________________________________________________________________________________________________
batch_normalization_275 (BatchN (None, 14, 14, 224) 672 conv2d_306[0][0]
__________________________________________________________________________________________________
activation_275 (Activation) (None, 14, 14, 224) 0 batch_normalization_275[0][0]
__________________________________________________________________________________________________
conv2d_304 (Conv2D) (None, 14, 14, 192) 399360 block8_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_307 (Conv2D) (None, 14, 14, 256) 172032 activation_275[0][0]
__________________________________________________________________________________________________
batch_normalization_273 (BatchN (None, 14, 14, 192) 576 conv2d_304[0][0]
__________________________________________________________________________________________________
batch_normalization_276 (BatchN (None, 14, 14, 256) 768 conv2d_307[0][0]
__________________________________________________________________________________________________
activation_273 (Activation) (None, 14, 14, 192) 0 batch_normalization_273[0][0]
__________________________________________________________________________________________________
activation_276 (Activation) (None, 14, 14, 256) 0 batch_normalization_276[0][0]
__________________________________________________________________________________________________
block8_4_mixed (Concatenate) (None, 14, 14, 448) 0 activation_273[0][0]
activation_276[0][0]
__________________________________________________________________________________________________
block8_4_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_4_mixed[0][0]
__________________________________________________________________________________________________
block8_4 (Lambda) (None, 14, 14, 2080) 0 block8_3_ac[0][0]
block8_4_conv[0][0]
__________________________________________________________________________________________________
block8_4_ac (Activation) (None, 14, 14, 2080) 0 block8_4[0][0]
__________________________________________________________________________________________________
conv2d_309 (Conv2D) (None, 14, 14, 192) 399360 block8_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_278 (BatchN (None, 14, 14, 192) 576 conv2d_309[0][0]
__________________________________________________________________________________________________
activation_278 (Activation) (None, 14, 14, 192) 0 batch_normalization_278[0][0]
__________________________________________________________________________________________________
conv2d_310 (Conv2D) (None, 14, 14, 224) 129024 activation_278[0][0]
__________________________________________________________________________________________________
batch_normalization_279 (BatchN (None, 14, 14, 224) 672 conv2d_310[0][0]
__________________________________________________________________________________________________
activation_279 (Activation) (None, 14, 14, 224) 0 batch_normalization_279[0][0]
__________________________________________________________________________________________________
conv2d_308 (Conv2D) (None, 14, 14, 192) 399360 block8_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_311 (Conv2D) (None, 14, 14, 256) 172032 activation_279[0][0]
__________________________________________________________________________________________________
batch_normalization_277 (BatchN (None, 14, 14, 192) 576 conv2d_308[0][0]
__________________________________________________________________________________________________
batch_normalization_280 (BatchN (None, 14, 14, 256) 768 conv2d_311[0][0]
__________________________________________________________________________________________________
activation_277 (Activation) (None, 14, 14, 192) 0 batch_normalization_277[0][0]
__________________________________________________________________________________________________
activation_280 (Activation) (None, 14, 14, 256) 0 batch_normalization_280[0][0]
__________________________________________________________________________________________________
block8_5_mixed (Concatenate) (None, 14, 14, 448) 0 activation_277[0][0]
activation_280[0][0]
__________________________________________________________________________________________________
block8_5_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_5_mixed[0][0]
__________________________________________________________________________________________________
block8_5 (Lambda) (None, 14, 14, 2080) 0 block8_4_ac[0][0]
block8_5_conv[0][0]
__________________________________________________________________________________________________
block8_5_ac (Activation) (None, 14, 14, 2080) 0 block8_5[0][0]
__________________________________________________________________________________________________
conv2d_313 (Conv2D) (None, 14, 14, 192) 399360 block8_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_282 (BatchN (None, 14, 14, 192) 576 conv2d_313[0][0]
__________________________________________________________________________________________________
activation_282 (Activation) (None, 14, 14, 192) 0 batch_normalization_282[0][0]
__________________________________________________________________________________________________
conv2d_314 (Conv2D) (None, 14, 14, 224) 129024 activation_282[0][0]
__________________________________________________________________________________________________
batch_normalization_283 (BatchN (None, 14, 14, 224) 672 conv2d_314[0][0]
__________________________________________________________________________________________________
activation_283 (Activation) (None, 14, 14, 224) 0 batch_normalization_283[0][0]
__________________________________________________________________________________________________
conv2d_312 (Conv2D) (None, 14, 14, 192) 399360 block8_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_315 (Conv2D) (None, 14, 14, 256) 172032 activation_283[0][0]
__________________________________________________________________________________________________
batch_normalization_281 (BatchN (None, 14, 14, 192) 576 conv2d_312[0][0]
__________________________________________________________________________________________________
batch_normalization_284 (BatchN (None, 14, 14, 256) 768 conv2d_315[0][0]
__________________________________________________________________________________________________
activation_281 (Activation) (None, 14, 14, 192) 0 batch_normalization_281[0][0]
__________________________________________________________________________________________________
activation_284 (Activation) (None, 14, 14, 256) 0 batch_normalization_284[0][0]
__________________________________________________________________________________________________
block8_6_mixed (Concatenate) (None, 14, 14, 448) 0 activation_281[0][0]
activation_284[0][0]
__________________________________________________________________________________________________
block8_6_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_6_mixed[0][0]
__________________________________________________________________________________________________
block8_6 (Lambda) (None, 14, 14, 2080) 0 block8_5_ac[0][0]
block8_6_conv[0][0]
__________________________________________________________________________________________________
block8_6_ac (Activation) (None, 14, 14, 2080) 0 block8_6[0][0]
__________________________________________________________________________________________________
conv2d_317 (Conv2D) (None, 14, 14, 192) 399360 block8_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_286 (BatchN (None, 14, 14, 192) 576 conv2d_317[0][0]
__________________________________________________________________________________________________
activation_286 (Activation) (None, 14, 14, 192) 0 batch_normalization_286[0][0]
__________________________________________________________________________________________________
conv2d_318 (Conv2D) (None, 14, 14, 224) 129024 activation_286[0][0]
__________________________________________________________________________________________________
batch_normalization_287 (BatchN (None, 14, 14, 224) 672 conv2d_318[0][0]
__________________________________________________________________________________________________
activation_287 (Activation) (None, 14, 14, 224) 0 batch_normalization_287[0][0]
__________________________________________________________________________________________________
conv2d_316 (Conv2D) (None, 14, 14, 192) 399360 block8_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_319 (Conv2D) (None, 14, 14, 256) 172032 activation_287[0][0]
__________________________________________________________________________________________________
batch_normalization_285 (BatchN (None, 14, 14, 192) 576 conv2d_316[0][0]
__________________________________________________________________________________________________
batch_normalization_288 (BatchN (None, 14, 14, 256) 768 conv2d_319[0][0]
__________________________________________________________________________________________________
activation_285 (Activation) (None, 14, 14, 192) 0 batch_normalization_285[0][0]
__________________________________________________________________________________________________
activation_288 (Activation) (None, 14, 14, 256) 0 batch_normalization_288[0][0]
__________________________________________________________________________________________________
block8_7_mixed (Concatenate) (None, 14, 14, 448) 0 activation_285[0][0]
activation_288[0][0]
__________________________________________________________________________________________________
block8_7_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_7_mixed[0][0]
__________________________________________________________________________________________________
block8_7 (Lambda) (None, 14, 14, 2080) 0 block8_6_ac[0][0]
block8_7_conv[0][0]
__________________________________________________________________________________________________
block8_7_ac (Activation) (None, 14, 14, 2080) 0 block8_7[0][0]
__________________________________________________________________________________________________
conv2d_321 (Conv2D) (None, 14, 14, 192) 399360 block8_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_290 (BatchN (None, 14, 14, 192) 576 conv2d_321[0][0]
__________________________________________________________________________________________________
activation_290 (Activation) (None, 14, 14, 192) 0 batch_normalization_290[0][0]
__________________________________________________________________________________________________
conv2d_322 (Conv2D) (None, 14, 14, 224) 129024 activation_290[0][0]
__________________________________________________________________________________________________
batch_normalization_291 (BatchN (None, 14, 14, 224) 672 conv2d_322[0][0]
__________________________________________________________________________________________________
activation_291 (Activation) (None, 14, 14, 224) 0 batch_normalization_291[0][0]
__________________________________________________________________________________________________
conv2d_320 (Conv2D) (None, 14, 14, 192) 399360 block8_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_323 (Conv2D) (None, 14, 14, 256) 172032 activation_291[0][0]
__________________________________________________________________________________________________
batch_normalization_289 (BatchN (None, 14, 14, 192) 576 conv2d_320[0][0]
__________________________________________________________________________________________________
batch_normalization_292 (BatchN (None, 14, 14, 256) 768 conv2d_323[0][0]
__________________________________________________________________________________________________
activation_289 (Activation) (None, 14, 14, 192) 0 batch_normalization_289[0][0]
__________________________________________________________________________________________________
activation_292 (Activation) (None, 14, 14, 256) 0 batch_normalization_292[0][0]
__________________________________________________________________________________________________
block8_8_mixed (Concatenate) (None, 14, 14, 448) 0 activation_289[0][0]
activation_292[0][0]
__________________________________________________________________________________________________
block8_8_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_8_mixed[0][0]
__________________________________________________________________________________________________
block8_8 (Lambda) (None, 14, 14, 2080) 0 block8_7_ac[0][0]
block8_8_conv[0][0]
__________________________________________________________________________________________________
block8_8_ac (Activation) (None, 14, 14, 2080) 0 block8_8[0][0]
__________________________________________________________________________________________________
conv2d_325 (Conv2D) (None, 14, 14, 192) 399360 block8_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_294 (BatchN (None, 14, 14, 192) 576 conv2d_325[0][0]
__________________________________________________________________________________________________
activation_294 (Activation) (None, 14, 14, 192) 0 batch_normalization_294[0][0]
__________________________________________________________________________________________________
conv2d_326 (Conv2D) (None, 14, 14, 224) 129024 activation_294[0][0]
__________________________________________________________________________________________________
batch_normalization_295 (BatchN (None, 14, 14, 224) 672 conv2d_326[0][0]
__________________________________________________________________________________________________
activation_295 (Activation) (None, 14, 14, 224) 0 batch_normalization_295[0][0]
__________________________________________________________________________________________________
conv2d_324 (Conv2D) (None, 14, 14, 192) 399360 block8_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_327 (Conv2D) (None, 14, 14, 256) 172032 activation_295[0][0]
__________________________________________________________________________________________________
batch_normalization_293 (BatchN (None, 14, 14, 192) 576 conv2d_324[0][0]
__________________________________________________________________________________________________
batch_normalization_296 (BatchN (None, 14, 14, 256) 768 conv2d_327[0][0]
__________________________________________________________________________________________________
activation_293 (Activation) (None, 14, 14, 192) 0 batch_normalization_293[0][0]
__________________________________________________________________________________________________
activation_296 (Activation) (None, 14, 14, 256) 0 batch_normalization_296[0][0]
__________________________________________________________________________________________________
block8_9_mixed (Concatenate) (None, 14, 14, 448) 0 activation_293[0][0]
activation_296[0][0]
__________________________________________________________________________________________________
block8_9_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_9_mixed[0][0]
__________________________________________________________________________________________________
block8_9 (Lambda) (None, 14, 14, 2080) 0 block8_8_ac[0][0]
block8_9_conv[0][0]
__________________________________________________________________________________________________
block8_9_ac (Activation) (None, 14, 14, 2080) 0 block8_9[0][0]
__________________________________________________________________________________________________
conv2d_329 (Conv2D) (None, 14, 14, 192) 399360 block8_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_298 (BatchN (None, 14, 14, 192) 576 conv2d_329[0][0]
__________________________________________________________________________________________________
activation_298 (Activation) (None, 14, 14, 192) 0 batch_normalization_298[0][0]
__________________________________________________________________________________________________
conv2d_330 (Conv2D) (None, 14, 14, 224) 129024 activation_298[0][0]
__________________________________________________________________________________________________
batch_normalization_299 (BatchN (None, 14, 14, 224) 672 conv2d_330[0][0]
__________________________________________________________________________________________________
activation_299 (Activation) (None, 14, 14, 224) 0 batch_normalization_299[0][0]
__________________________________________________________________________________________________
conv2d_328 (Conv2D) (None, 14, 14, 192) 399360 block8_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_331 (Conv2D) (None, 14, 14, 256) 172032 activation_299[0][0]
__________________________________________________________________________________________________
batch_normalization_297 (BatchN (None, 14, 14, 192) 576 conv2d_328[0][0]
__________________________________________________________________________________________________
batch_normalization_300 (BatchN (None, 14, 14, 256) 768 conv2d_331[0][0]
__________________________________________________________________________________________________
activation_297 (Activation) (None, 14, 14, 192) 0 batch_normalization_297[0][0]
__________________________________________________________________________________________________
activation_300 (Activation) (None, 14, 14, 256) 0 batch_normalization_300[0][0]
__________________________________________________________________________________________________
block8_10_mixed (Concatenate) (None, 14, 14, 448) 0 activation_297[0][0]
activation_300[0][0]
__________________________________________________________________________________________________
block8_10_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_10_mixed[0][0]
__________________________________________________________________________________________________
block8_10 (Lambda) (None, 14, 14, 2080) 0 block8_9_ac[0][0]
block8_10_conv[0][0]
__________________________________________________________________________________________________
conv_7b (Conv2D) (None, 14, 14, 1536) 3194880 block8_10[0][0]
__________________________________________________________________________________________________
conv_7b_bn (BatchNormalization) (None, 14, 14, 1536) 4608 conv_7b[0][0]
__________________________________________________________________________________________________
conv_7b_ac (Activation) (None, 14, 14, 1536) 0 conv_7b_bn[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_3 (Glo (None, 1536) 0 conv_7b_ac[0][0]
__________________________________________________________________________________________________
dense_12 (Dense) (None, 516) 793092 global_average_pooling2d_3[0][0]
__________________________________________________________________________________________________
dropout_6 (Dropout) (None, 516) 0 dense_12[0][0]
__________________________________________________________________________________________________
dense_13 (Dense) (None, 256) 132352 dropout_6[0][0]
__________________________________________________________________________________________________
dropout_7 (Dropout) (None, 256) 0 dense_13[0][0]
__________________________________________________________________________________________________
dense_14 (Dense) (None, 64) 16448 dropout_7[0][0]
__________________________________________________________________________________________________
dense_15 (Dense) (None, 2) 130 dense_14[0][0]
==================================================================================================
Total params: 55,278,758
Trainable params: 13,383,078
Non-trainable params: 41,895,680
__________________________________________________________________________________________________
None
# Define modifier to replace the sigmoid function of the last layer to a linear function
def model_modifier(m):
m.layers[-1].activation = tf.keras.activations.linear
# Define losses functions. 0 is the index for a normal MRI
loss_normal = lambda output: K.mean(output[:, 0])
# Define losses functions. 2 is the index for a PVNH MRI
loss_PVNH = lambda output: K.mean(output[:, 1])
# Create Gradcam object
gradcam = Gradcam(model, model_modifier)
# Create Saliency object
saliency = Saliency(model, model_modifier)
# Iterate through the MRIs in test set
# Set background to white color
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='white'
plt.rcParams['figure.edgecolor']='white'
print('\n \n' + '\033[1m' + 'EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW)' + '\033[0m' + '\n')
print('\033[1m' + 'EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI OR PERIVENTRICULAR NODULAR HETEROTOPIA) \n \nHIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI' + '\033[0m'+ '\n')
# print images 200 to 299
for i in range(200, 300):
# Print spaces to separate from the next image
print('\n \n \n \n \n \n')
# Print real classification of the image
if y_true[i]==0:
real_classification='Normal MRI'
else:
real_classification='PVNH'
print('\033[1m' + 'REAL CLASSIFICATION OF THE IMAGE: {}'.format(real_classification) + '\033[0m')
# Print model classification and model probability of MCD
if y_predInceptionResNetV2[i]==0:
predicted_classification='Normal MRI'
else:
predicted_classification='PVNH'
print('\033[1m' + 'MODEL CLASSIFICATION OF THE IMAGE: {}'.format(predicted_classification) + '\033[0m \n')
print('\033[1m' + ' Prob. Normal MRI: {:.4f} '.format(testInceptionResNetV2[i][0]) + 'Prob. PVNH: {:.4f}'.format(testInceptionResNetV2[i][1]) + '\033[0m')
# Arrays to plot
original_image=shuffled_test_X[i]
list_heatmaps=[
# GradCam heatmap for normal MRI
normalize(gradcam(loss_normal, shuffled_test_X[i])),
# GradCam heatmap for PVNH
normalize(gradcam(loss_PVNH, shuffled_test_X[i])),
# Saliency heatmap for normal MRI
normalize(saliency(loss_normal, seed_input=np.expand_dims(shuffled_test_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for PVNH
normalize(saliency(loss_PVNH, seed_input=np.expand_dims(shuffled_test_X[i], axis=0), smooth_noise=0.2))
]
# Define figure
f=plt.figure(figsize=(20, 8))
# Define the image grid
grid = ImageGrid(f, 111,
nrows_ncols=(2, 2),
axes_pad=0.05,
share_all=True,
cbar_location="right",
cbar_mode=None,
cbar_size="2%",
cbar_pad=0.15)
# Iterate over the graphs
for j, axis in enumerate(grid):
# Plot original
im=axis.imshow(original_image)
im=axis.imshow(list_heatmaps[j][0], cmap='jet', alpha=0.5*testInceptionResNetV2[i][j%2])
im=axis.set_xticks([])
im=axis.set_yticks([])
# Create scalarmappable for obtaining the colorbar from 0 to 1
sm = plt.cm.ScalarMappable(cmap='jet', norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
plt.show()
EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW) EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI OR PERIVENTRICULAR NODULAR HETEROTOPIA) HIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9991 Prob. PVNH: 0.0009
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.4000 Prob. PVNH: 0.6000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0033 Prob. PVNH: 0.9967
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.4007 Prob. PVNH: 0.5993
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9969 Prob. PVNH: 0.0031
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9922 Prob. PVNH: 0.0078
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0002 Prob. PVNH: 0.9998
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0006 Prob. PVNH: 0.9994
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0092 Prob. PVNH: 0.9908
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0006 Prob. PVNH: 0.9994
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0001 Prob. PVNH: 0.9999
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9981 Prob. PVNH: 0.0019
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0020 Prob. PVNH: 0.9980
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0001 Prob. PVNH: 0.9999
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0034 Prob. PVNH: 0.9966
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9953 Prob. PVNH: 0.0047
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0014 Prob. PVNH: 0.9986
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9997 Prob. PVNH: 0.0003
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0042 Prob. PVNH: 0.9958
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.5442 Prob. PVNH: 0.4558
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.7540 Prob. PVNH: 0.2460
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9996 Prob. PVNH: 0.0004
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0122 Prob. PVNH: 0.9878